{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Temporal Fusion Transformer\n",
    "In this notebook, we show two examples of how two use Darts' `TFTModel`.\n",
    "If you are new to darts, we recommend you first follow the `darts-intro.ipynb` notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fix python path if working locally\n",
    "from utils import fix_pythonpath_if_working_locally\n",
    "\n",
    "fix_pythonpath_if_working_locally()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm_notebook as tqdm\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from darts import TimeSeries, concatenate\n",
    "from darts.dataprocessing.transformers import Scaler\n",
    "from darts.models import TFTModel\n",
    "from darts.metrics import mape\n",
    "from darts.utils.statistics import check_seasonality, plot_acf\n",
    "from darts.datasets import AirPassengersDataset, IceCreamHeaterDataset\n",
    "from darts.utils.timeseries_generation import datetime_attribute_timeseries\n",
    "from darts.utils.likelihood_models import QuantileRegression\n",
    "\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import logging\n",
    "\n",
    "logging.disable(logging.CRITICAL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Temporal Fusion Transformer (TFT)\n",
    "\n",
    "Darts' `TFTModel` incorporates the following main components from the original Temporal Fusion Transformer (TFT) architecture as outlined in [this paper](https://arxiv.org/pdf/1912.09363.pdf):\n",
    "\n",
    "- gating mechanisms: skip over unused components of the model architecture\n",
    "- variable selection networks: select relevant input variables at each time step.\n",
    "- temporal processing of past and future input with LSTMs (long short-term memory)\n",
    "- multi-head attention: captures long-term temporal dependencies\n",
    "- prediction intervals: per default, produces quantile forecasts instead of deterministic values\n",
    "\n",
    "### Training\n",
    "`TFTModel` can be trained with past and future covariates. It is trained sequentially on fixed-size chunks consisting of an encoder and a decoder part:\n",
    "\n",
    "- encoder: past input with `input_chunk_length`\n",
    "  - **past target**: mandatory\n",
    "  - **past covariates**: optional\n",
    "- decoder: future known input with `output_chunk_length`\n",
    "  - **future covariates**: mandatory (if none are available, consider `TFTModel`'s optional arguments `add_encoders` or `add_relative_index` from [here](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tft_model.html?highlight=tftmodel#temporal-fusion-transformer-tft))\n",
    "\n",
    "In each iteration, the model produces a quantile prediction of shape `(output_chunk_length, n_quantiles)` on the decoder part.\n",
    "\n",
    "### Forecast\n",
    "#### Probabilistic Forecast\n",
    "Per default, `TFTModel` produces probabilistic quantile forecasts using `QuantileRegression`.\n",
    "This gives the range of likely target values at each prediction step.\n",
    "Most deep learning models in Darts'  - including `TFTModel` -  support `QuantileRegression` and 16 other likelihoods to produce probabilistic forecasts by setting `likelihood=MyLikelihood()` at model creation.\n",
    "\n",
    "To produce meaningful results, set `num_samples >> 1` when predicting. For example:\n",
    "\n",
    "```\n",
    "model.predict(*args, **kwargs, num_samples=200)\n",
    "```\n",
    "\n",
    "Predictions with forecasting horizon `n` are generated auto-regressively using encoder-decoder chunks of the same size as during training.\n",
    "\n",
    "If `n > output_chunk_length`, you have to supply additional future values for the covariates you passed to `model.train()`.\n",
    "\n",
    "#### Deterministic Forecast\n",
    "To produce deterministic rather than probabilistic forecasts, set parameter `likelihood` to `None` and `loss_fn` to a PyTorch loss function at model creation. For example:\n",
    "\n",
    "```\n",
    "model = TFTModel(*args, **kwargs, likelihood=None, loss_fn=torch.nn.MSELoss())\n",
    "...\n",
    "model.predict(*args, **kwargs, num_samples=1)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# before starting, we define some constants\n",
    "num_samples = 200\n",
    "\n",
    "figsize = (9, 6)\n",
    "lowest_q, low_q, high_q, highest_q = 0.01, 0.1, 0.9, 0.99\n",
    "label_q_outer = f\"{int(lowest_q * 100)}-{int(highest_q * 100)}th percentiles\"\n",
    "label_q_inner = f\"{int(low_q * 100)}-{int(high_q * 100)}th percentiles\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Air Passenger Example\n",
    "This data set that is highly dependent on covariates. Knowing the month tells us a lot about the seasonal component, whereas the year determines the effect of the trend component.\n",
    "\n",
    "Additionally, let's convert the time index to integer values and use them as covariates as well. \n",
    "\n",
    "All of the three covariates are known in the future, and can be used as `future_covariates` with the `TFTModel`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read data\n",
    "series = AirPassengersDataset().load()\n",
    "\n",
    "# we convert monthly number of passengers to average daily number of passengers per month\n",
    "series = series / TimeSeries.from_series(series.time_index.days_in_month)\n",
    "series = series.astype(np.float32)\n",
    "\n",
    "# Create training and validation sets:\n",
    "training_cutoff = pd.Timestamp(\"19571201\")\n",
    "train, val = series.split_after(training_cutoff)\n",
    "\n",
    "# Normalize the time series (note: we avoid fitting the transformer on the validation set)\n",
    "transformer = Scaler()\n",
    "train_transformed = transformer.fit_transform(train)\n",
    "val_transformed = transformer.transform(val)\n",
    "series_transformed = transformer.transform(series)\n",
    "\n",
    "# create year, month and integer index covariate series\n",
    "covariates = datetime_attribute_timeseries(series, attribute=\"year\", one_hot=False)\n",
    "covariates = covariates.stack(\n",
    "    datetime_attribute_timeseries(series, attribute=\"month\", one_hot=False)\n",
    ")\n",
    "covariates = covariates.stack(\n",
    "    TimeSeries.from_times_and_values(\n",
    "        times=series.time_index,\n",
    "        values=np.arange(len(series)),\n",
    "        columns=[\"linear_increase\"],\n",
    "    )\n",
    ")\n",
    "covariates = covariates.astype(np.float32)\n",
    "\n",
    "# transform covariates (note: we fit the transformer on train split and can then transform the entire covariates series)\n",
    "scaler_covs = Scaler()\n",
    "cov_train, cov_val = covariates.split_after(training_cutoff)\n",
    "scaler_covs.fit(cov_train)\n",
    "covariates_transformed = scaler_covs.transform(covariates)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a model\n",
    "If you want to produce deterministic forecasts rather than quantile forecasts, you can use a PyTorch loss function (i.e., set `loss_fn=torch.nn.MSELoss()` and `likelihood=None`).\n",
    "\n",
    "The `TFTModel` can only be used if some future input is given. Optional parameters `add_encoders` and `add_relative_index` can be useful, especially if we don't have\n",
    "any future input available.<br>\n",
    "They generate endoded temporal data is used as future covariates.\n",
    "\n",
    "Since we already have future covariates defined in our example they are commented out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# default quantiles for QuantileRegression\n",
    "quantiles = [\n",
    "    0.01,\n",
    "    0.05,\n",
    "    0.1,\n",
    "    0.15,\n",
    "    0.2,\n",
    "    0.25,\n",
    "    0.3,\n",
    "    0.4,\n",
    "    0.5,\n",
    "    0.6,\n",
    "    0.7,\n",
    "    0.75,\n",
    "    0.8,\n",
    "    0.85,\n",
    "    0.9,\n",
    "    0.95,\n",
    "    0.99,\n",
    "]\n",
    "input_chunk_length = 24\n",
    "forecast_horizon = 12\n",
    "my_model = TFTModel(\n",
    "    input_chunk_length=input_chunk_length,\n",
    "    output_chunk_length=forecast_horizon,\n",
    "    hidden_size=64,\n",
    "    lstm_layers=1,\n",
    "    num_attention_heads=4,\n",
    "    dropout=0.1,\n",
    "    batch_size=16,\n",
    "    n_epochs=300,\n",
    "    add_relative_index=False,\n",
    "    add_encoders=None,\n",
    "    likelihood=QuantileRegression(\n",
    "        quantiles=quantiles\n",
    "    ),  # QuantileRegression is set per default\n",
    "    # loss_fn=MSELoss(),\n",
    "    random_state=42,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the TFT\n",
    "\n",
    "In what follows, we can just provide the whole `covariates` series as `future_covariates` argument to the model; the model will slice these covariates and use only what it needs in order to train on forecasting the target `train_transformed`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fe7b9ff3dda44e418b3ca62eb7835520",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/300 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training loss: 0.0549\r"
     ]
    }
   ],
   "source": [
    "my_model.fit(train_transformed, future_covariates=covariates_transformed, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look at predictions on the validation set\n",
    "We perform a one-shot prediction of 24 months using the \"current\" model - i.e., the model at the end of the training procedure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def eval_model(model, n, actual_series, val_series):\n",
    "    pred_series = model.predict(n=n, num_samples=num_samples)\n",
    "\n",
    "    # plot actual series\n",
    "    plt.figure(figsize=figsize)\n",
    "    actual_series[: pred_series.end_time()].plot(label=\"actual\")\n",
    "\n",
    "    # plot prediction with quantile ranges\n",
    "    pred_series.plot(\n",
    "        low_quantile=lowest_q, high_quantile=highest_q, label=label_q_outer\n",
    "    )\n",
    "    pred_series.plot(low_quantile=low_q, high_quantile=high_q, label=label_q_inner)\n",
    "\n",
    "    plt.title(\"MAPE: {:.2f}%\".format(mape(val_series, pred_series)))\n",
    "    plt.legend()\n",
    "\n",
    "\n",
    "eval_model(my_model, 24, series_transformed, val_transformed)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Backtesting\n",
    "Let's backtest our `TFTModel` model, to see how it performs with a forecast horizon of 12 months over the last 3 years:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "307d0dd38ec74d7e89e980af4ef48363",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/3 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "backtest_series = my_model.historical_forecasts(\n",
    "    series_transformed,\n",
    "    future_covariates=covariates_transformed,\n",
    "    start=train.end_time() + train.freq,\n",
    "    num_samples=num_samples,\n",
    "    forecast_horizon=forecast_horizon,\n",
    "    stride=forecast_horizon,\n",
    "    last_points_only=False,\n",
    "    retrain=False,\n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE: 5.24%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def eval_backtest(backtest_series, actual_series, horizon, start, transformer):\n",
    "    plt.figure(figsize=figsize)\n",
    "    actual_series.plot(label=\"actual\")\n",
    "    backtest_series.plot(\n",
    "        low_quantile=lowest_q, high_quantile=highest_q, label=label_q_outer\n",
    "    )\n",
    "    backtest_series.plot(low_quantile=low_q, high_quantile=high_q, label=label_q_inner)\n",
    "    plt.legend()\n",
    "    plt.title(f\"Backtest, starting {start}, {horizon}-months horizon\")\n",
    "    print(\n",
    "        \"MAPE: {:.2f}%\".format(\n",
    "            mape(\n",
    "                transformer.inverse_transform(actual_series),\n",
    "                transformer.inverse_transform(backtest_series),\n",
    "            )\n",
    "        )\n",
    "    )\n",
    "\n",
    "\n",
    "eval_backtest(\n",
    "    backtest_series=concatenate(backtest_series),\n",
    "    actual_series=series_transformed,\n",
    "    horizon=forecast_horizon,\n",
    "    start=training_cutoff,\n",
    "    transformer=transformer,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Monthly ice cream sales\n",
    "Let's try a another data set. That of monthly ice cream and heater sales since 2004. Our target is to predict future ice cream sales.\n",
    "First, we build the time series from the data, and check its periodicity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(True, 12)\n",
      "(True, 12)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 648x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "series_ice_heater = IceCreamHeaterDataset().load()\n",
    "\n",
    "plt.figure(figsize=figsize)\n",
    "series_ice_heater.plot()\n",
    "\n",
    "print(check_seasonality(series_ice_heater[\"ice cream\"], max_lag=36))\n",
    "print(check_seasonality(series_ice_heater[\"heater\"], max_lag=36))\n",
    "\n",
    "plt.figure(figsize=figsize)\n",
    "plot_acf(series_ice_heater[\"ice cream\"], 12, max_lag=36)  # ~1 year seasonality"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Process the data\n",
    "We again have a 12-month seasonality. This time we will not define monthly future covariates -> we let the model handle this itself!\n",
    "\n",
    "Let's define past covariates instead. What if we used past data of heater sales to predict ice cream sales?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert monthly sales to average daily sales per month\n",
    "converted_series = []\n",
    "for col in [\"ice cream\", \"heater\"]:\n",
    "    converted_series.append(\n",
    "        series_ice_heater[col]\n",
    "        / TimeSeries.from_series(series_ice_heater.time_index.days_in_month)\n",
    "    )\n",
    "converted_series = concatenate(converted_series, axis=1)\n",
    "converted_series = converted_series[pd.Timestamp(\"20100101\") :]\n",
    "\n",
    "# define train/validation cutoff time\n",
    "forecast_horizon_ice = 12\n",
    "training_cutoff_ice = converted_series.time_index[-(2 * forecast_horizon_ice)]\n",
    "\n",
    "# use ice cream sales as target, create train and validation sets and transform data\n",
    "series_ice = converted_series[\"ice cream\"]\n",
    "train_ice, val_ice = series_ice.split_before(training_cutoff_ice)\n",
    "transformer_ice = Scaler()\n",
    "train_ice_transformed = transformer_ice.fit_transform(train_ice)\n",
    "val_ice_transformed = transformer_ice.transform(val_ice)\n",
    "series_ice_transformed = transformer_ice.transform(series_ice)\n",
    "\n",
    "# use heater sales as past covariates and transform data\n",
    "covariates_heat = converted_series[\"heater\"]\n",
    "cov_heat_train, cov_heat_val = covariates_heat.split_before(training_cutoff_ice)\n",
    "transformer_heat = Scaler()\n",
    "transformer_heat.fit(cov_heat_train)\n",
    "covariates_heat_transformed = transformer_heat.transform(covariates_heat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a model with automatically generated future covariates and train it\n",
    "\n",
    "Since we don't have future covariates defined, we need tell the model to generate future covariates itself.\n",
    "\n",
    "- `add_encoders`: can add multiple encodings as past and / or future covariates from datetime attributes, cyclic repeating temporal patterns, index positions and customm functions for index encodings. You can even add a transformer that handles proper scaling of the training, validation and prediction data! Read more about it in the `TFTModel` docs [from here](https://unit8co.github.io/darts/generated_api/darts.models.forecasting.tft_model.html#temporal-fusion-transformer-tft)\n",
    "- `add_relative_index`: adds a scaled integer position relative to the prediction point for each encoder-decoder chunk (this might be useful if you really don't want to use any future covariates. The position values remain constant over all chunks and do not add additional information).\n",
    "\n",
    "We use `add_encoders={'cyclic': {'future': ['month']}}` to account for the 12-month seasonality as a future covariate.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "04cf171092ad4ef88b3ec52ee3636aa8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/300 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training loss: 0.1257\r"
     ]
    }
   ],
   "source": [
    "# use the last 3 years as past input data\n",
    "input_chunk_length_ice = 36\n",
    "\n",
    "# use `add_encoders` as we don't have future covariates\n",
    "my_model_ice = TFTModel(\n",
    "    input_chunk_length=input_chunk_length_ice,\n",
    "    output_chunk_length=forecast_horizon_ice,\n",
    "    hidden_size=32,\n",
    "    lstm_layers=1,\n",
    "    batch_size=16,\n",
    "    n_epochs=300,\n",
    "    dropout=0.1,\n",
    "    add_encoders={\"cyclic\": {\"future\": [\"month\"]}},\n",
    "    add_relative_index=False,\n",
    "    optimizer_kwargs={\"lr\": 1e-3},\n",
    "    random_state=42,\n",
    ")\n",
    "\n",
    "# fit the model with past covariates\n",
    "my_model_ice.fit(\n",
    "    train_ice_transformed, past_covariates=covariates_heat_transformed, verbose=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Look at predictions on the validation set\n",
    "Again, we perform a one-shot prediction of 24 months using the \"current\" model - i.e., the model at the end of the training procedure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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btozvfe97gNrYd+7ced57I5EIra2t/OhHPwJUlCOdTnPbbbfx9a9/nWRSqfuurq7hdY/FeNZ/00038fDDDw+fa3BwkOPHj9Pf349t27zxjW/k4x//OC+99NKE7oFGM9+xsxaWpUXGxXAExtKGlQDEY7FSLkczAi0wgHvuuYerrrqKAwcO0Nrayte+9rVRj3vjG9/I+vXrufPOO/k//+f/UF1dDcB3vvMdVq9ezdq1a2lubh72G9x4443s3bv3LJPneKioqODFF19k48aNPP744/zDP/wDAA8++CBf+9rX2Lx581m+j3P51re+xRe+8AU2bdrEq171Kk6fPs2tt97KW9/61uFy2rvvvvui4sExjzomz9FYv349H//4x7n11lvZtGkTt9xyC93d3XR1dXHDDTewZcsW3v72t48a4dBoNGNjGzaGoQXGhUgmk0SjUbxeP62RZgASOoJRNuhx7TPARMf0hsPh4UjDfGWOjzaeFvQ9mxzleN+Geg2e+MYAN723gcpqd6mXcx7lcs8OHDjA2rVrWbBwBZ+69z+575PX0NKyjJMnj5Z6aedRLvdsmtDj2jUajWa2YFsSIz8vn8nGjVPJV1vXTCgQASCZmnhaWjM9aIFRhsz36IVGowHblJg5LTAuhOO/qKtuJuQPA5DWAqNs0AJDo9FoyhBp6gjGxXAERn3lQnz+AB63F8PInVXCrykdWmBoNBpNGWJZYOZ1N88L4QiMBZEFCL+boF+V60+mek9TfLTA0Gg0mjJEag/GRXEERlOwEfxuQn7lw9C9MMoDLTA0Go2mDBG2JJfREYwLceKEEhgNVQsRLkFFUAmMwUHdC6Mc0AKDsce1Dw4Ocsstt7Bq1SpuueUWhoaGRn3/448/ztatW9m4cSP33nvvcHdOKSV//ud/ztVXX82mTZuGm011dHTw7W9/e/j93/jGN7j//vun6bsrPj/60Y+Gx7YD/MM//AOPPfYYwJgD0TQazQSRkmxaC4wLMezBqFoIQCigjJ7RqI5glANaYDD2uPZPfvKT3HTTTRw6dIibbrqJT37yk+cdY9s29957L9/97nfZvXs3S5Ys4T//8z8BeOSRRzh06BC//e1veeCBB/iTP/kT4HyBMRMUc4z7uQLjn/7pn7j55puLdn6NRqPIp3WKZCwymQyDgwO43V5qIvUAhAoRjGhcezDKAS0wGHtc+49//GPuvfdeAO69997h9tsjGRgYwOfzsXq1muTnjGt33v+Od7wDIQRXXnkl0WiU7u5uPvzhD/P000+zZcuW4THmp06d4jWveQ2rVq3iQx/60KjrXLp0KR/60Ie45JJLuPzyyzl8+DAAfX19vPGNb+Syyy7jsssu45lnngHgox/9KH/4h3/I1VdfzR/+4R/S09PDG97wBjZv3szmzZt59tlngdHHrYNq+PW3f/u3bN68mSuvvJKenh6effZZfvKTn/DBD36QLVu2cOTIEe67777h1uYjefTRR7nqqqvYunUrb3rTm4bLbz/84Q+zfv16Nm3axF/91V+N7x9Jo5mH5FJWqZdQtnR1dQFQV70QV9AL6AhGuVFWw85+XvfLaTnvawdum9T7enp6WLhQhd6ampro6ek575j6+npM06S9vZ22tjYefvjh4eYvXV1dLFq0aPjY1tZWurq6+OQnP8lnPvMZfvrTnwIqRbJjxw5efvll/H4/a9as4c/+7M/Oeq9DVVUVu3bt4pvf/CZ/+Zd/yU9/+lP+4i/+gg984ANcc801dHZ2ctttt7Fv3z4A9u7dy29/+1uCwSBvectbuP766/nhD3+IZVkkk8mzxq17vV7+9E//lAcffJB3vOMdpFIprrzySv7lX/6FD33oQ3zlK1/h7/7u77jrrru44447uPvuu8e8d/39/Xz84x/nscceo6Kigk996lN89rOf5f3vfz8//OEP2b9/P0IIotHopP5tNJr5QF4PPBuTMyWqTeBTz8qhYCWgR7aXC2UlMMoZIQRCnN8NVQjBd7/7XT7wgQ+Qy+W49dZbh8e4T4SbbrqJqqoqQM33OH78+KgC45577hn+/wc+8AEAHnvssbNSFvF4fDhacNdddxEMqpHPjz/+ON/85jcBNTW1qqqKb33rW6OOWwfw+XzccccdgBrj/qtf/Wrc38/zzz/P3r17ufrqqwHI5/NcddVVVFVVEQgEePe7380dd9wxfH6NRnM2LhcY2uQ5Jo7AaKhciPAUBEZIRTC0wCgPykpgTDbSMF0sWLCA7u5uFi5cSHd39/DGe9ttt9HT00NbWxtf/epXueqqq3j66acBlRY4ePAgAC0tLZw4cYJly5YB6heipaWFAwcOnHet8Y5xHylynI9t2+b5558nEAicd/x4xriPNW7d6/UOX2O8o+VHnveWW27hO9/5znmvvfjii/z617/m4Ycf5t///d95/PHHx31ejWa+INyCfEanSMai47iKFDdULxz+mhPBiGqBURZoD8YFuOuuu4YNm//5n//J6173OgB++ctfsmPHDr761a8CZ8a153I5PvWpTw2Pa7/rrrv45je/iZSS559/nqqqKhYuXDjpMe7A8FTWhx56iKuuugqAW2+9lS9+8YvDx+zYsWPU995000186UtfAsCyLGKx2Jjj1i/EeNZ/5ZVX8swzzwz7RFKpFAcPHiSZTBKLxXjta1/L5z73uVFHzms0GnB7BXbOZoYHUs4aOo4XIhgjBIZTpqpHtpcHWmAw9rj2D3/4w/zqV79i1apVPPbYY3z4wx8e9f2f/vSnWbduHZs2beLOO+/k1a9+NQCvfe1rWb58Oddccw3vec97+L//9/8CsGnTJtxuN5s3bx42eY6XoaEhNm3axOc///nh937hC1+gvb2dTZs2sX79er785S+P+t7Pf/7z/OY3v+GSSy5h27Zt7N27d8xx6xfiD/7gD/j0pz/NpZdeypEjR0Y9pqGhgW984xvcc889bNq0iauuuor9+/eTSCS444472LRpE9dccw2f/exnJ/T9azTzBpdAGhJTBzFG5cQJZfJsqGse/ppTRRKL6SqSckCPa58BijWmd+nSpbS3t1NfX1+EVZU3c3y08bSg79nkKLf7ls1mefOb3soizzXceOlbeO1fLSQUKq9nwXK4ZxsuaWPv7u184cOPsH618pA9+bsf889fejevvukN/PqxH5R0fedSDvdsGtHj2jUajabceeKJJ/ifn/6Qnz33VUBg6Imqo9J9SqVIGhtah7/mRDASCe3BKAfKyuSpuTAdHR2lXoJGo5lmXnnlFQCSmSgCMPI2MPHKtLlMPp9naLAHl8tNTVXj8NcrAkpgJLXAKAu0wNBoNJoyYteuXYASGBL0wLNRONZRaLIVacTtUuJr58+iZAMqfZxMag9GOaAFhkaj0ZQRnS8F+UTTLzkl4hhWDiOrBca5HO0oNNmqVgbPkwezrP3NYU77Vc+fdEpHMMoB7cHQaDSaMsEwDLq7TrMpL1ltV5NMxbQHYxSOHisIjEIFSd+eNADV+RwA6UyyNAvTnMVFIxhtbW1VwK+A9cCV7e3tu0e85ga+AqwCtre3t//lNK1To9Fo5jwHDhwgmuuEEFTagoFsFCOnu3mey5GjncCIEtXOFAABaeN3VZHNxjFNC49He1dKyXgiGGngduD8aVZwB3Cqvb39WqCira3tqmIuTqPRzH6OHTtGPp8v9TJmBa+88grxvNo8I7ZNOjtEVs8jOY/Oo4UunjVKYNT1nUmJ1IfWIqUkFk+VZG2aM1xUYLS3txvt7e19Y7z8KuDRwse/AK4u1sI0Gs3s55VXXmH58uW8//3vL/VSZgWvvPIKOXuIvBD4pU0imSSrJ6qeR5cz6Ky2mdiAyYJsZvi1utAKAAYHtQ/DIZ2VvHRg5iNhUzV51gDOv2IMOG/meVtb23uB9wLcf//93HLLLVO85OzDMIzh0cKa8aHv2cQpx3v2y1+qCck7duwou7U5lNN9e/HFFwFIuNzUWSbehhw1rVG6usqrKqKU90xKSCXVOIP1V9YQ3xdj5BSm+grVF6Oz8zCh4Kj9n0pCKe+ZaUGFG6br8mM1EJuqwIgClYWPq4DBcw9ob29/AHig8Om8jPXN8Q5u04K+ZxOnHO/Z0NAQAMlksuzW5lBO9+3QoUMAJD0u6izo3uXixM4wN7/1vGe3klLKexZNSE6eOK0+6V1B7zMGjSNej9AEgCUDZfPvCqW9Zyd7bLqOGbz6ev/FDy4iU60ieRa4ufDxbcAzUzyfRqOZQzizahyhoRmbwcFBTp48STAYJOtXf5qNjCCvR7afxVA8TzTegxCC2uoFhE6p6E6/T22ela46QM8jGUlq0MA4PvOelHEJjLa2tp8DtwJfaWtru6+tre3/FV76KbC4ra3taSDb3t7+3DStU6PRzEKOHj0KqM1TTwW9ME6DrY0bN5IvCAyyLkxt8jyLjkNdSCmpqWwA6aE5roRET6uK8oSF6uYZ1RNVh0nEJNKc+Z+jcaVI2tvbX3vOl75R+LoJ3FfcJWk0mrmCIzAMwyCVShEOh0u8ovLFERibNm3CfEkJDFfeg6kjGGdx9KCqsqmvaabrQJ6Ftk1PIIhYEISjELFDAES1yXOYZMIGc+Z/jnSjLY1GMy1Eo1EGB8/YsnSa5MI4M0guueQSRET1b/AaXmzDxrJ0FMOh89iZEtXB/arB1kB9BHdY3bMK2wvA0JCOYDikoxbYM/8zpAWGRqOZFo4dO3bW5yPFhuZ8HIGxadMm3FUquBwwfWBKjBKEt8uVrhMFgVHbgutEoWPnkjD+wj2rsJTQiA3pCAaAlJJUrDSlzlpgaDSaacFJjzhogTE2tm2ze7dqknzJJZcQalCGxaDlBYluF15ASklPt6q1bKhdSH2/EhF16yoIFKI+4cJeGotpgQFgmGAmzZJcWwsMjUYzLTgVJA46RTI2x44dI5VK0dzcTH19PVVNykdQYXtA6ImqDnkDBgZUk60qzyoaclnSLjfNK31UVKhjwqZSGIm4riIByObBTusIhkajmUM4EQwhVLMjHcEYm5H+C4D6xcoMG7Hd2LaFqSMYAOQMGBw6BYA/ugyAU9WVeDyCiBqkSsRUT+uJhPZggLpnIl8ao7AWGBqNZlpwBMbatWsBLTAuxEj/BUBNs+pNGbEFmWxCRzAKZHOSgVg3AME+dY+yzUqMeX2CtMuNB0mFeyHJpI5gAGRTNsLQAkOj0cwhHIGxbds2QAuMCzGyRBWgvtUHQKUtSWaimFpgAJCMGQwlegCo7lcD9EIrVW5EACmvqiCp9C0hldQeDIBU0kZYWmBoNJo5gmmaHD+u5kVs3boV0B6MC3FuBKO21YsNhG2LWDquIxgFThzvxrJN6ioX05pQEYrWjcHhJm4ZX0FgeFtIp+K6uRuQiNu4SzS1XgsMjUZTdE6ePIlpmrS0tAzPX9ARjNFJpVIcPnwYj8cznE7yhlwkXWpXiEfT5HSzLQCOHlEGz/W1r8crJaeCFUSq3WBKRMhDzl8QGJ4mMpkEph5ESzKmBYZGo5lDOBUky5cvp6amBtACYyz27NmDlJK1a9fi86nUiBCCZGFXSCcMclktMABOHFcCY6X3SgCGGlVbcPIWVHoxQkpgVLkayGSTWmAAqYSNp0Q7vRYYGo2m6Dj+i+XLl1Nbq2ZE6BTJ6Jzrv3BIelT1TS4tyaa0wIAzTbaWGgsAcC0p1Kbmbaj0YocLEQxXLZmsjmBIKUnHLDze0oyt1wJDo9EUndEEho5gjM65JaoO6cKkKCsjyGuBgZSS3h5VQbJMdQinfr3qFyIBV8gDlU4EoxrDzJFM5Uqx1LIhb4DMmODRAkOj0cwRtMAYP+caPB1yXucDLTAAcnmIRXtpCGymzjRIuj0sXFZIKUkg4MZdpW5atcuZqDq/S1WzeXDlLHCXZqvXAkOj0RSdkQIjEongcrlIJBIYhlHilZUXUsoxBUber5463XkPhvZgkDMgHu1lXcXvAdBdU4nbLZC2BJcAvxtvbaG8V6iuW/N94FnOALKWjmBoNJq5gyMwVqxYgcvlGjZ6RqPREq6q/Oju7mZwcJCamprhahsHK6RMnj7DTV5XkZAzIJboZ61XCbFci2qwhWEjwh6ESxBoKHgwpMovRQfmt8DI5iTJPoMDXz7Jzm/1zfj1tcDQaDRFxRnTHgqFaGxsBNBpkjE4a0S7OOcpM+JMVPVgZu1539Mhm7aJJfpYRwMA4VUjDJ6F1EioUUUwIpa6l9HB+d1sKxm3SAxYrOkboOZEdMavrwWGRqMpKiPTI86mqUtVR2es9AiAu0pNVA3ZHoRhY83ziohE3CKdNViet7GARRtVq3BMGyJKYIQXqntWWehcOTA4vyMY8bgkH1NpyXTQN+PX1wJDo9EUlZECw0GXqo7OWCWqAIF65SOosNxgSwxjfkcwek4nWOq/CQ+S7oowoXChe5QEV1BFeyqafNhAxLZw4SU6NL8jGKm4jRVXw9/yYS0wNBrNLOdCAkNHMM5mrBJVgPCCSkANPAMw5vlE1a6Tp1kVuByAmNNgyyGgxIbX7yLlVtGMiG8xsXlu8kzFbUgogSGrtMDQaDSznJEGTwctMM4nn8+zb98+ADZu3Hje6+FmtYlGbDDMLPl5Po+ku6uHBS7l6aFBpUKkJcHjAt+ZrWx44Jl3EfH4/I1g2LYkk7DwpNRQOHe19yLvKD5aYGg0mqIyWgTD8WDoFMkZDhw4gGEYrFixgnA4fN7roaZCyaVlkUjHMOdxBMO2JX2nTlMvVFTHW1N4Gs9biIj3LINsxplH4m4mkZi/AiNfKFENpJXACNZpgaHRaGY5I+eQOOgIxvlcyH8B4At7yAgXHiRDscS8TpHkDYgO9FAvC8bXhkKb0xEVJMPHBtRrVZ6FJOPzt9GWarJlEsmqbqbhhpmfeKYFhkajKRojx7QvXbp0+OtaYJzPhfwXALgEicLAs2g0i5mfv70wnCZbDYXy06pGJSqkLRGRswWGGXIiGPUkE/N3ZHvOADtpUZ1XEYyaRs+Mr0ELDI1GUzROnDiBZVm0tLQQCASGv65TJOezd+9eADZs2DDmMUm32lDTMYN8bv4KjGweEoNRaiwTC0F1YbMUgAic/WQuw2fmkaTT8Xlb3pszINFj4EES83jxBWZ+u9cCQ6PRFI3R/BegIxij4Rg8169fP+YxzsAzI2OSy87PJ3GAbF6SiyvhMOT14hnZ+vocgSEqC94VVyWZ9PydqJpIWKQGVA+MuN9fkjVogaHRaIrGaBUkoAXGuWSzWY4ePYrb7WbVqlVjHpfxKFFhZ+b3yPZkwsaVUVU18YKJU5o2+FwI79nbmFMtUUmIbCY+fwVGVJIv9MBIh7TA0Gg0s5yxIhi6k+fZHDx4ENu2WbFiBf4LPF3mCi+JnIt8eh4LjJhNwKgGIF3wWJC3z/NfAPjqHIERIJ2dzxEMG7uETbZACwyNRlNERqsggbM9GPPVdDcSx39xofQIgBVQqQCP4Z7XAiORsKmwqwAwKwuqK29D9fkbZ6BBfa1Keshmk/NWYKQTNiRUikRWznyJKmiBodFoishYEQy/309FRQWmaZJMJkuxtLLCERjr1q274HGyQgkMv+EiP49HtscG89RIJTA8NaqFupQSV8X5G2fFgoIHwxakc8l52aDMtiXZtI03pQSGpwRNtkALDI1GU0TGEhigfRgjGY/BE8BV6PEQsNzk56kHw7Ylvd09NKA8GL46JSAEnGfwBIg0Ow3KbLK5JLnM/Ath5AwQGYtAWvXACJSgyRZogaHRaIrE0NAQQ0NDVFRUDI9pH4kuVT3DeFMkzsZQYbkx52kEI2dAYrCHelsJh4oG75k02yill8E6D4YQBKWN1xUhNjT/mm1l8yDyJpVZ1QMjUjfzTbZACwyNRlMkjh07Bpw9pn0kOoKhMAyDgwcPArBmzZoLHlvRGAIgbAnsvI1tz79wfy4Psb4e6gtD36oWeMCUiJAH4T5/C3N73SQ8hYFn3sUM9EdncrllQS4PZtKiyshhA9WNWmBoNJpZzIXSI6AFhsORI0cwTZOlS5dSUVFxwWOrFynfQdgGach5aVjMGRDt7afaMjERVNV7IG/BBYyLmcLAsyrvYoYG5988kpwBia4cbiDu9eH1lWar1wJDo9EUhbEqSBx0ikQx3vQIQONydc8ilo20LYx5aFhUHSmVshpuspW3Lygwss7AM8/CeSkw4imbdF+hyVagND0wQAsMjUZTJHQEY3w4Bs+LVZAAhBcEMRGEpE08GceYhz6MeMrGjKkQ/1DhSVwCrtDYszWMQCGC4W4kOjT/BEYiJjFipW2yBVpgaDSaIqEFxviYSATD7XUNDzwbHEzOywhGMiZxZ9UmmSyYOoVk1AoSB6sgPirdtURjsWlfY7mRjNtYCSUwzIhPKbISoAWGRqMpCmO1CXfQAkMx3h4YDs7As/hQZl4KjETMxp9Tg/PyIRfSluAS4B9bYMhIoVRVVJOIzb8IRippIwpNtqjygS3Br4edaTSaWYgzpl0IwZIlS0Y9RnswwLIs9u/fD0xEYKj/ZxJ5zNz8ExiplE3EVM21qPKCYSPCHoTr/EolB+HMI3FFSEbnl8CwLEkubeNNqhJVb60XDAkXSClNF1pgaDSaKTPWmPaR6AgGHD9+nGw2S3NzM9XV1eN6T9qtRIWRmn8mT2ezrLJUiiRYH7iowRPOdK6sIkQqFZtX5b05A0TeIpRRAqOiwQuWrQWGRqOZnVysggS0wICJp0cAst7CRNWsJDfPTJ45A2TWpM5SgqG6JQKGDaMMORuJ06CsUvrIppMY5rQvtWzI5sGVtajMqi6elQuUsBDeme+FoQWGRqOZMhczeIJOkcD4W4SPJO9TAsOdF+Sy8+dJHJTAyEaHqLfU993QHMK2JdYoDbZGEmxwBIaHTGp+jWzP5cFIGFSaeSygprEgxkrQC0MLDI1GM2UuZvAEHcGAyUUwzJDaXD15F9nEPNopUZtl38nTVNkWBoLKejfxrGAo66JvSI6Z+jgz8AwymfklMLJ5SaIrjwuIef0YVop3fvJK/vdn3ottz2wETAsMjUYzZTo6OgBYunTpmMdEIhHcbjfJZBLDMGZmYWXGeCMYXveZykIRVn+mA6Ygl5l/KZLB42qWyKDXg9stwJa0XeJmaTN0D0Iifb7IiDQrz0alZZHOJOaVwEhmINOr/BfxoJ+dB56lN3qSzuP7cLlmdsvXAkOj0UyZU6dOAdDS0jLmMUKIeZ0mkVKOuweG33fmPZ5qlTsPWi7y80xgJNKSdK/yEgx6hBpyJgQ1dS4uW+vitssEHjec6pfkzTNCw1/lIeNy40Vi5NzzyhwbS0jyg0rAZyv8vLT7SQC2XnHLjK9FCwyNRjNlurq6gAsLDJjfaZKuri4SiQR1dXU0NDRc8FghBOEAGCYEapXaCFkuzPkmMJISK67EQdxngyWRPhfBgCpRra8W3HqZ4PJ1EE3CYOFY3IKkR5kb3WbVvBIYybiNPaLJ1kv7lMC49PKbZnwtWmBoNJopIaXUAmMcTNTgGamAvAHhJjUQLWyBMd8ERtzGnVbbVDpgIQ0bGfTgH1FE4nYLVra6eO0Vgnwh8yaEIO1VAsNrV82rFuvJuI0rqW6EUWHTcWo/fl+IdZdcNeNr0QJDo9FMicHBQXK5HFVVVRedDuoIjPmYIplIi3CAyhDkTahdXAlAxJYYmfnzJA6QStgEc0oomBUSM28TrPbgGqXJVjgkcLvBKhg/cwUV4rMqycwTYWaaEjNr4y0IjAFLpS43rn8VPt/MzyQZV+eNtra2TwGvAjqAd7W3txuFrweB/wYqARN4a3t7e8/0LFWj0ZQj441ewJlS1fkYwZhoBUmkIDAWrKwDDhG2TDI5C8uSyuw4x1FNtiQRww2YuKtdWDlJRe3Y/RzCQZVWcvvACCqBEaKS3DwRZtk8CMsmlFG+lY7YDgAu3Tbz6REYRwSjra1tM9DS3t5+LbAfuHvEy78H7G5vb78e+Abw7ulYpEajKV8cgdHc3HzRY3WKZPwRjFBAICVU1IRJudy4gdhgmrwxPzbLnAHCtKkxlJgKNXgwDYjUXFxgAMiw8q6EqSIZz077esuBnAEia1GZUwJjR+fPANhSrgIDFbl4tPDxL4CrR7x2GHBiojVAf/GWptFoZgMTiWDMZ4Ex0QiG36tmerlcLhKF8sLBgQTGPJlHkjOAvEWtqdIb9c0hTAmVVWNvW5WhMwKDcKFduKuaocH5MY8kZ4ARM6gyDUwExweepzpcz5JVl5RkPeNJkdQA3YWPY0DtiNcOAevb2tr2AAK4/Nw3t7W1vRd4L8D999/PLbfMfKlMqTEMY/iPsGZ86Hs2cUp1z5wn88rKyoteXwj1NHrixImy+fedifs2MDDAwMAA4XAYYFzXs21Y3wxeD7zilmBCsDHFULSbRLK0KZKZuGeGCcuWmvTbFoYQbLvdT1Ukjy/cT1fX6N9/xAurFqg+IuFWCduhUoQJRjrp6pr5VtkjmYl7ljOgpkZFL4a8XmxMrr7mapa39uN2C6br8mM9XIxHYERRHguAKmDko8e9wG/b29s/2tbWdjfw98Bfj3xze3v7A8ADhU/nh/Q+h66urnE93WnOoO/ZxCnVPUsmkwCsXbv2otd3Wonn8/my+fedift2+PBhADZs2EBra+u43mOakmeelDTVChIuJeJOvmQScC9gQcuFZ3FMNzNxz46dsnnuB8dZBfS73YSOttB9Ms+Nf9ZE3Rg+jFP9khePqHtmek8CUEmAfS/Dra8p7c/bTNyznYdsDvyqkzXAQGF3X1tzA/t7mlnRDGtayq/R1rPAzYWPbwOeGfGa4ExapB8lQDQazTxCp0guzmRahHs8Ap8HTEuS8ag0gZGePxNVk1mIn1CpjQG3jZACPC6CobG3rYBPbUoA/hEDz+LzZGR7NCkxBlUXz16phP+2TTeQN6HywgVe08JFBUZ7e/sOoKetre1pYAPw/ba2tv9XePnbwB1tbW1PAP8MfHaa1qnRaMqUyVSRzLcy1ckMOYNCqaoBWW+hzDIrMeeJwEikIduXAWDIYyBNCxl0E/CN/R6/F2Th9gQbC/NIpItUPK66gM5xBgclMq5KVHvtIRY1rqSxdQlICPpnPq02rjLV9vb2D57zpT8ufD0GvKbYi9JoNLMHHcG4OBPtgeEQCcHpQTD8NuDGnWPeRDASSRsZV47NuDeHmZP4Kr2j9sBw8HsZDmFEFhYEhiWHJ6p6x7XjzU5yeUk2beMqdPHst/vYuvpaCKl00oWE2XShG21pNJpJk8vl6Ovrw+1209jYeNHj56vAcCIYE0mRQKGbpwlWYaKq1xDk0vNjclcqJvGmVeQm689h52wqai+sEDwegdetemiEF3ixgYhtkUrO/YFnyQy4TRtvSqVI+s2TbF11LSLkQaIFhkajmWV0d6sCs6amJtzui7v0R6ZI5kPIGiAajXLq1CmCwSBLliyZ0HsjQYFlgQirx/KAKcjOg66UhinJZWxChZJcuyKHYUgiF2iy5eCIMk/QQ8rtwQVkY/aZ8tU5SiItkcaZJlv95jE2Lb8K6RG4xJkBejOJFhgajWbSTCQ9AuD1egmHw1iWRSKRmM6llQ1O9GLNmjXjEmEjcTYFb40zUVWQH2U8+VwjmQGXaVNVSAd5qixMCyov0GTLIRJUAgOPIFG430bSPecjGIMJ8El7uMlWpMFFOFSFIVyEg2dKxGcSLTA0Gs2kmajAgPmXJpmswRMKYW0BoXqlNCosyKXmfgQjmQZMm9pC2KGyQYAUVEQuvmWFC822hEuQ8qjjZdY7570rfVGQ8RyVlomBYN3aFSAlhstFZag0a9ICQ6PRTJpTp9QwJS0wxmayBk9geGpoZKHaISKWJJ+e+wJjICaxonnCtkVeCOoWVICQBMMXj2BUhgRmIR2S9amndpfhx8jN3ftm25JoApInVfRiwONm28brQEAeV0lKVEELDI1GMwUmE8GYb6Wqk+mB4eArtAsfnqhqWfNiZHtfDDJdBS+B201NZR0IQTB88TD/yFHuOZ/a4ryGn9wcvm+prCrP7Tmg2lL1uW3WLt4KATeGLbTA0Gg0sw+dIrk4U0mRCCGIhKCyqZa8EPilTWJobrsVbVsyGIdMd0FguCxqKhqQAfe4ejn4feDYDcyg2uJ8lp9Uau6mSFIZwLTpPqZM13GfxIsHEVJVN6XogQFaYGg042b37t1UV1fzpS99qdRLKRu0wLgwqVSKjo4OvF4vK1asmNQ5wgHwh2uJu1R6INabm9MVOM7TeLZHCYw+kSbsq8YXcY9rTH3AN2ImRVhtcUHLTzI5d+9ZLCUROYvsUAoAM+wBU0LIjaA0JaqgBYZGM24efvhhYrEYjzzySKmXUjZMZFS7gyMw5kOK5NixYwAsW7YMr3dy80MqK8CWbhKFv9bx/uScrohIpgEpyQ+kARhyJXFZXoI14+uS5aRIpJS4qtQnFbaX9ByOYPRFIWAZ+NKF0fYLqsCwoYQ9MEALDI1m3Lz44osAnD59usQrKQ+klFPyYMyHCMZIgTFZnL4OSbfaIFPRDIYxdzfLaFLiMmyIq4ZRCXccI28TrhufwHC5BEEfmBb46wIAhG0vmTlcfTMQg+5Dr1Bjq++3uqUWKQCfq2Q9MEALDI1mXEgphwVGT09PiVdTHgwNDZHNZolEIkQikXG/bz6lSDo6OgBYunTppM8R9AmkhJRLhS1yyRxGbu4KjL4oBG0Tf1rN1Mj64pgmVI2jB4aDU6oaaSpU39huMnFrTqaW8oYknYNXXnqcBlspiXCjDwEl7YEBWmBoNOPi2LFjDAwMAEpgzMU/VBNlMtELmL8pksni96lKkoxHCQw7bc3pksv+GPhNk0hOmVntUBJbQkXl+AWG02yrulUJ30pbYKXyc7KbZzIDLinZufe31Fvq71JNkwckGC43kRL1wAAtMDSaceFEL0DN34jFYiVcTXkwmR4YoFMkE8UxLea8TnMHm/wcjWBkc5KcAe64QU1eRTB8lRkEEAyPf7uKFCIYVS1BACotm3wiSeGUc4pkBmTOon8gRti2yAkXkRp3oQeGoDpcurVpgaHRjIPf/e53Z32u0yRTj2DMB4FRjBSJY1pUE1XBnZPkM3NTYCQzahhqsitLxfBmqW5AaBxdPB0qCjNcQnUeTAQhaZPsi6oW4nOMwZjEZ1rIrPq9ivr8CCnB48LEpSMYGk2540QwnFkSWmBogTEeihHBcLuVadGZqOozmLMDzxJpiTAsoqdUqKHf7aKmsg7pcxMMjN9H4FRNuHwu4oXf2WhfiuwcjPz0xYBElLBUlVzJkB8MiQi5kbJ0PTBACwyN5qKYpsn27dsBuOaaawAtMGDyAmO+dPKMRqPEYjFCoRD19fVTOlc4BKIQ6g5agmR8bgqMvhj4bYvEoAo19LlNqkP1uMMePJ7xb5R+b6HZlkeQKPTOSEfz5OZY5MdpShY71UmDV03qzVX4wFIlqojSlaiCFhgazUXZs2cPmUyG5cuXD3dj1KWqk+uBARAOh/F4PKRSKXKFyY9zkZHRi6m6+CMhcFWqP9chE1KxudkIoz8KPtMkW+hW2k+aiL+WinGMaR+J36t8K8LtIlEo782lLFLJuXXf0oWmZP0nT1DnXgCAXaUiGIQ8SKkFhkZT1jj+i8svv5wFC9QvsY5gTD6CIYSYF5UkxUiPOFSGwFfrTFSVpIbm1kYJYJqSeBp8yTx2oiAw5BBhXx3h2vH1wHBw+j5IKUm5VbTHykhScyzyk8oChk1/fxcNQv1Ouaq9SnX43bhdap5NqdACQ6O5CI7/4vLLL6epqQnQAgMmLzBgfpSqFsPg6RAOCkJOTwfLIjsHIxjJTOGDuIFIFjwYVi/hQAORCUYwhBBUBFQliVPeKzKQnmPtwmNJicib9MW6WSBVDs1f50UCeZcyeJaqBwZogaHRXJSRAsOJYMz3FIlhGPT29uJyuYbvyUSYD6WqxYxg+H1Q1RzBBjXCPG5iWXNrs0xmQBo2ZCx8SZU6G7RP4fdWEKmamMAAlVbKm5AJKLHiybhIJ+dWBKM/DgHTpC/azQpT3aPGlYHhJlulrCABLTA0mguSSqXYvXs3brebSy+9VKdICnR3q6mNTU1NeDwTC1/D/KgkKWYEI+CDyroGkoWBZ5khg2x+bgmMwbjEa1ogoSKtBEbW24cQrgn1wHBwemHIiLpPwZwgM8ciP/1R8GcMUtEgEdsi6vFS26R+H/PCRVWJxrQ7aIGh0VyAl19+Gcuy2LhxI6FQSAuMAlNJj8D8SJEUNYLhhXCkloTLqYiwyKXnlsDojULQMrFtSU1ezSGxA4OAJDiBHhgOkSDkDfA3KBNCOA92xsQ058Z9yxuSVBa8KYNQWv2MdUeCCAAhMBFUVpQuPQJaYGg0F2SkwRM4S2DM53bhUxUYcz1FIqUsqsDweQXBUJCES/3MpRN5snNoeJeUqtzSlzEYikqCtkXS5SYYluB2EQxNfKsKBQQSqFqsvAk1JoisRW6OdPNMZVWLcJkyaTIXAZBdEATTRoTcIERJK0hACwyN5oKM9F8AVFRUEA6H53278MmWqDrM9RRJf38/6XSaqqoqqquri3LOSAgShYFnqXiaXHruCIx0FmwbXHGDng4VvTjksaiO1OEKufF5J/4k7lSSLFqnepDUmhYyY82Zbp7JNNgZi3w+y3KrDoDwiiowJQTciBL3wAAtMDSaC+IIjMsuu2z4azpNUrwUyVwVGMWMXjiEgxB3q83XTOTnVE+HZAawJTJhkD2RBeAQ3VQG6wnWTNzjA4XNVULrylbSLhcBaRPvyZCbI96VoYTEZ1n0RrtZWTB4LlyrIhhUeJBogaHRlC39/f0cPXqUYDDIhg0bhr9eylLVbDZLvpCfLiXag3FhHINnMQVGVQXEPYVazqQ1p3o6xJISV84EAf7TSQAO5XdTGayjon5yAsOZ4eIJ+Oh3WvyfHpgz3pXeKATyBsc7EoRti0G3h5oFHrAkdqD0PTBACwyNZkza29sB2LZt21mVEqUqVTUMg3Xr1nHttdfO6HVHQ3swLowTwShGBYlDOAgpXxoAf8aaU822eqMQsC0sS9IULQiM9K+JBOqpnGAPDAevB9xusFwQ9SgxFu9Pkp4DkR/Hs+JN54l1qfTR8YDA5VK+E9PtJhwsbQ8MgMlJQ41mHnCu/8KhVCmS7u5uOjo6OHHiBFLKkv7xmOyodgedIpk4Qb/ADOegD0JZe0412xqIQSBnMNBnUW0ZxNwe+rI7iITqqayenMAQQhAOSoyci3ih2VY+ZpJMzP7ITzoLtgRXwsQ74AFM+sIqtSSk6oFRV+ISVdARDI1mTEbzX0DpBIZzPcuySKfTM3rtkUgpdYrkIhSzB4ZDwAeeBiUqK/M2mdjccCvm8pJ0DjwJg97jqsTjiE+JgOqKegKTKFF1CAfBlIKMXwkMV8omPQdSS6kskLPAsGlIKgGWryukz0R59MAALTA0mlGRUo4ZwXA8GDOdIhkpaOLx+IxeeySxWIx0Ok04HKaysnJS55gvKZJiRjD8Pgg3+wGoNQzslIkxB3o6JDMgpETGDfIn1VP4YakauVVFGiZVouoQCYKBwKpQ98mfE6TmQHlvPCUROQtLwpKMEpqVS01VOi/BwFXyHhigBYZGMyqdnZ309fVRV1d33iZR6ggGlFZgTDV6AWePbLft2f8HfyS2bXP8+HGguBEMvxcaljSSFi6C0iYds8jlZr/ASKSlehq3JcFe5b/Yn23HJVxEgjWTarLlUBmGvCXwVSs3QDjvIhud/aml/hgELJPeLpMK26Lf7aFlSTVYEvwuhLv0PTBACwyNZlRGRi/O9TqUSmCMjJiUsgfHVHtgAHg8HiorK7Ftm0QiUayllQWnT58ml8tRX19POBwu2nldLkHrksUMeNSf7eSgNSeabfXHwGdaWLakKVYweKYeY+XiTXjDfvy+yT+JB3wCKaGqKQBAtSkwUtasn+PSHwVf2qDvuKooO+LJ0VDTDKZEBD1QBj0wQAsMjWZUxvJfQOnKVOdSBAPmbppkOtIjDkuWLqLPpULiqSFzzgiMgGHQ221RYZkMerwM5vZx6ZrrCVS7p2Rm9nvBJWDhctWIqs60cWVnd7Mtw5QkMqqCxDilvFiHZBe1VY1g2BByI6UWGBpN2TKW/wLOLlOdyXbhc01gzNVKkukweDo0LqhgUCgzX3owNeu7eVqWJJYEXyp/5mncq4yem5ddT6h2aoWOAR9IoGXFAgwhiNgWiYEE+VncLjyRBmHZkLYI9qYA6BKHcLsLPTCCHjxuJtX9tNhogaHRnINlWWzfvh0YPYIRCoUIh8Pk8/kZTVXMNYFRV6eeKvv6+qa8pnJiOiMYlSFB3K0Ehh3LkprlJZeprDJUEzcxT6nv64B1nKC/guULt1A5ySZbDn4vSAm+Ch+DbnWu7mP9s3oSbTylPCuWLWlOKIGRCB0CQNoSw+Mu+Zh2By0wNJpz2LdvH6lUiqVLl9LY2DjqMaXwYYy8Vik9GFPtgeGwaJEa0NTZ2TnlNZUT09HF06EiKMj4VaWFJ22QnOXNthJpkHkbDJtQ4Wn8YO5ltqy7BrfwUVkzuR4YDh6PwOcB2+di0KNExVB/YlZ38+yNgt+2OH3CJGRb9Hk8RGrOCE3D5aKyDEpUQQsMjeY8LuS/cChFqerIa82FCMaSJUuAuScwpqOLp4PfC2aFiu+Hsjbp6Cw2EwADMYnXsDAsycK4MngeTv2Ktg2vBgmBiqkJDIBwCAyPm7hXiYp81CAzi7t5nh6AYN5koJBSOuzOUl+zEAAB5F1uKnUEQ6MpTy7kv3CY6QhGLpcjGo0Ofz4XBMbixYsBhks65wrTmSIJ+MBbozbKSsMmE5+9GyVAzxCETIPeLpOgbdHr8RDLH2HbxhsQgimVqDpEQuqpPu0r3KuUPWtTS7m8JJUFb8rA7C4MhbNP0lDTjDRt8LmwPO6y6IEBWmBoNOexe/duALZs2TLmMTMtMHp7e8/6vFQpEsMw6OnpQQgxfA8mixPBmEsCwzRNTpw4AZz5/opJwAeVw822TLJRc0aNxsXEstQ8DV/GGH4aP+TO0FS/mJbG5UggFC6SwBAuzMJTvT/rmrUCI5EGgWpKVtGnUkqH8zuor22GnI2o8ZdNiSpogaHRnMehQ8owtXr16jGPmekUyblCplQRDKdyZsGCBXi9UxvVOBcFRldXF6ZpsnDhQgKBQNHP7/EIGpctICNchGyLzKCJMUuzJIm0qvAgZgwbPA/ZnWzbcAN23kYEPQSCU38SDwfAdLnwVKp0S9hwk0rOToERS0lEzsbMWzQlzqSUGmqaVbOyah8CLTA0mrIkHo/T29tLIBCgtbV1zONmOoLhXMflUr+ypRIYxUqPALS2tiKEGN6U5wLTafB0WLJ8Mf2FZluJAZPMLC1VTaRBGjbkLMKFp/FDuZfYtuFGzKRFuNlXlIF+Qb8At4tIgxJ8VaaL7Cz1rvQOQcA06TmhUkp9hZRSQ41qeucKe8qmBwZogaHRnMXhw4cBWLFixfBmPhqlEhiOcXAuCAy/309TUxOWZQ1Xpsx2ptPg6bB0xSIGRKHZ1qBJbpY22+qLSfymiWlKFjpP48lH2br+OvJZm/rF/qJcx+8DIaBpuWrsVmdCPmFj27MvtdQzBIFcnv7hlFIWIQR11U0gwAq4y6YHBmiBodGchZMeWbVq1QWPm+lunk4qxllXqTwYxRQYMPfSJNNp8HSoawgy6FIGv9RgctZ28+wZVE/j3cdN/NLmlMfD4iVNhENVmDbULpxaDwwHv1elYlrXLMAGqi2T9GB01jXbyuQkmRx44nmsYYNnJ7WVjbilG4JuDFxEyqREFbTA0GjOYrwCY2Q3z5nAETKOL6RUEYxi9cBwmGsCYzq7eDpU1biJedQGY8WyZGdhisQwJdEk+OJ5BjtzABxyp9i24QakLUEIKuuKJzAAfDU+Ym4PbqDr6OlZ1y7c6eAp4wYV/YWIT+5lZfDMWohqH3mTsilRBS0wNJqzcATGypUrL3jcyBTJTLj4HYHhCJ+5kCKBudcLYyYiGAG/IONTAsObNknGZl+paiKtRrQTzWMXnsYPWx20bbwR8jZ22EskXJwwv7swWdQOuBn0qHMOnYrPukm00YQa0X5WSin1aGHImQ01PvIGVBVvvt6U0QJDoxnBeCMYM90ufDSBUYryxGILjLnWC2MmTJ4BH1gjm23NQoGh2l3bYNqEe9U03eP2LtYu24qZMvE3+ovqIwgHwRQuYoVmW5moQS4zuyI/PYOqZ8jpEyYB26bX6yNuHKe+phkkuEJeTAsiRai8KRZaYGg0I3BMnhcTGDCzpaqOwGhtbSUYDGLbNqlUatqvey7FGNU+krmUIsnn85w8eRKXyzXcBn06CPjAU6f+dFcaNulZ2C68Nwp+0ySXtWlOp7CBikVp3G4P+bykpnlqJdDnEgmqZlspnxIYMiVnVTdPKSU9UfCn8gx0KoPniaB6rb5moWrhGXIjUKbWckELDI2mQCwWo6+vj2AwOK4NdCYrSRwRs2DBAiorK4GZT5NIKXWK5AJ0dnYipaS1tXXKPUIuhM8LVYtGNNuahRGMnkEIZvN0n7DwSkmX18PWS7YCYJjQ0Fwc/4VDpEK10HaabfmyblKJ2ZMiSWfBtMAVy2N1q54hJ30DADREmhAVHoRbbeeh4hTfFIVx/Su2tbV9CngV0AG8q7293Rjx2h8A70WJlb9pb29/bhrWqdFMO0565GIlqg4zJTDy+TxDQ0O43W7q6uqoqqqip6eHeDxetEjCeIjH46RSKUKhEFVVVUU558gUiZSyKH0PSsVMGDwBhBAsXtNI9qdQYVsMnM5j2xKXa3bcu1xeksxAYzzPUGeWZuCQK8G2DTciLRvpdlFdN/UZJCMJBwWWELgr3YBFheEhPYu6eSbSQNaCnD3cM6TDUh2H68NNUOtXKVMBwTISGBf9K9rW1rYZaGlvb78W2A/cPeK1ZuB1wE3t7e03aHGhmc2M13/hMFOlqk6b8IaGBlwu13AEY6ZLVZ0ZLYsWLSqaEKiqqqKqqop0Os3AwEBRzlkqZsLg6bB42WIGCk+s8V5zVhkWh6shkgb5zigAJz09LGxYAjkbu8pLJFRcsRTwAUJQ0ahCGNWmm1Ri9kR+BhMSd9bEGGHw3B/7GaAEhqhSBs9IUJlay4XxRDBeBTxa+PgXwDuB7xQ+fw2QA37V1tbWDfxJe3t7cuSb29ra3ouKcHD//fdzyy23FGPdswrDMIZDy5rxUYp79tJLLwFKOIzn2sGgSoIeOnRoWte6a9cuAGpra+nq6sLvV48oR48ePavb6HTeMyklf/3Xfw3A6173uqJep7m5mVgsxu9+9zs2bdpUtPOOl2LdN+ffqa6ubtp/djdt8vEjt0mLCcGGDH29p3B7Zm5jmco9yxmwsVXibjCp/ayqIPGvTNGyLYk0JU1eN7GhU8SGirdey4YNreC6KQhPRKkxBQuXDNLVNXNpxqncM5cBG1ZbHOo18EubvkCArp6XAdh8Vy3+mgEsBF43lGKrGStlOh6BUQN0Fz6OAbUjXlsA1AO3AH8C3A98cuSb29vbHwAeKHw6e2R2Eenq6ipaznq+UIp75kQitm7dOq5rO5GOdDo9rWvduXMnoCIHLS0tNDQ0AODz+c667nTesx//+Me8/PLLNDY28vd///eEw8WrhVu5ciX79u0jm82W5PekWPetv78fgE2bNk3795E1TYZcuwA/x5/L4P2T5SxsmTl331Tu2XO7bXr3pvEcjtKayWEBS1ctpGt7mFxvDve2WjZeUtw5Ltmc5NnfSir8AaCbesvixV+YvPNDi2csLTfZe2bbkmeekFQfGeDwL3KsA/qqQlinLKojDfQ9V4n7ugX0xWDzSmhpKR9r5XhWEgUqCx9XAYPnvPab9vZ2Cfwa2FDMxWk0M8lEUyQz5cFwzu9cz/E/zJTJ07IsPvKRjwDwd3/3d0UVFzB3SlVnMkUSqnATd6sGVWYsSz49e57deoYgkM5z5HASN5JOr4dtm68CIG9I6puKa/CEM5UVwQV+ki43Pik5dezUrBgUl8qCbUpcSQOrMBQuUaP+vRuqFyIqvQiXwJYUPbU0VcYjMJ4Fbi58fBvwzIjXngG2FD7eAhwt1sI0mplmvE22HGaqTHVkBQkw4x6MBx98kL1797J06VLe+973Fv38c6VUdaZMngABP2T8hWZbKYv0LCm5zOQkmazEHc/TtU8J5xP+HBXBCNKwMbweGhqKa/AEZYwNB8DyuBnyqPMPnojNinbhiTTIjAk2RAoGz3S1yh/VVzZBrVJPgvKqIIFxCIz29vYdQE9bW9vTqAjF99va2v5f4bVXgBNtbW1PAO8Cvjh9S9Vopo+hoSEGBgYIhULjrswoVQRjJstUc7kc//iP/wjAxz72sWH/RzGZCwIjk8lw+vRpvF7vjKR5Aj4ww+rxO5i1SUVnh8BIpEHkLDBsvD1qd0/VF6o5shay2kdF8afcAxAOgSFcRAsVxOmBPNls+Ud+BuISb9bAMCVNSWVxTAb2AVBftRBXRAkMW0Jomu7dZBlXLKq9vf2D53zpj0e89pGirkijKQFOg62VK1eOOyd7brvw6crlljJF8sADD9DR0cGGDRt429veNi3XmAu9MBxxtHjxYtzu4j+Bn4vfC9564DhUGTbJWdJsK5qQuLImlrRpTbsAi4Ub69WLho1c4CMcnJ5rR0IwKArNtlJgJyGXtqG2fDwLo3F6AELpPN1dFk3SptcfZCCt/l41VC2EkBvDlAR85TNF1aG876xGM0NM1H8Bql14JBIhn88TjUanaWVnBIaTkpmpCEYymeTjH/84AB//+MenbeOcCx6MmRjTPhKXS1CzRI3NrDVMsvFZYCag4L/IGuw7to9Ww8BAsOXK5YCq9PBXevD7pmeTrCxEMPKFVtrerFsJjDLGsiSDcfAmcwwVOngO1obpH1RDB+trmyHgJpeH2soLnak0aIGh0TBx/4XDTKRJxkqRTLcH4/Of/zy9vb1cccUVvO51r5u26yxYsACfz0d/f39J2p8XA+fnZyYMng7LNzSQEy7CtsXgyfIXGFJKeobAn8jxyo4uXEBnwIc/4EbakrwFtQuKb/B0CPoF0iUQVeoaFYaXZJk320pmQOYtRF5id6UBMJtD9A2pws7GVtWTJmtAbaSUKx0dLTA0GiYXwYCZERhjmTynM4IxMDDAv/7rvwLwiU98YlpL+Vwu13AUY7amSZ5++mkALr/88hm75rKVi+kvNNuKduexrPL2E6SzYOZs3BkT65Ra92BNwRCRt8n5PTTWTd/PWcAHSAgtUDmYKtNDqswFRiINpEykgHCf8l+El4XoHypEMAoRM8OE6iJNny0mWmBoNExsyNlIpltgGIbB4OAgLpeLuro6YGY8GJ/61KeIx+Pccsst3HjjjdN2HYfZLDCklDzxxBMA3HDDDTN23WXLmxlwKe9Foi9LNlv+m6VImwwl+mnO1QAQXKF+pslZ2FV+qiqmb5P0+0AIqF+mWjnVWm6S0fKO/PRGJf6cgWnCwqSK7i1c46PfiWAUfm+EKD+DJ2iBMW/593//d2pqati3b1+pl1IWTDaCMd2lqiPbhDseiOlOkXR1dfHFL6qCsP/1v/7XtFzjXGZzJcmePXvo7++nubl5wim2qVBd52ew0Asj1Z8kmyxvgTGUkLgyJr/b/zirLNWyu2GtiutLS0Klj3Bo+q4f8KlKiwVrlLipsyR9Xb3Td8Ei0DMI/mSeU10mPmnTEwhiuYYwLYNIqJqAY7wowwoS0AJj3vKlL32JaDTKI488UuqllJzBwUEGBwepqKgYFgzjZbojGOcaPGH6UyTf+MY3yGaz/P7v/z5tbW3Tco1zmc0Cw4le3HjjjTM6rC0YEMQ9Z5pt5cq82VbPEARTWbbv3UWTaZIVLppXOGXPEhl0T1uJKoDXI/C4IdziIy8EYdvi+KHyjZiZpiSWkHjTeYY6zhg8+wrpkYaaFoTfjWVLXK5CCqjM0AJjHtLd3c3evXuBM0/u85mRBs+JbhAzJTCc68D0C4wDBw4AcPvtt0/L+UdjLgiMmUyPgCpVzfiVwPCmLbKp8o1g2Lakd1DiiqVJd6tOsKciFXi8AmlJDOmiqt497RNhw0HVbGvQo4yevR1xDLM8hVkiA6RNhARZMHjazRVnBEaD6reSN6AmQllOItYCYx7y+OOPD3/seA/mM5NNj8D0p0guJjCkLP4fxyNHjgCwfPnyop97LGarB8O27bMiGDNJwAdWRHkwgjmbTBl380xlwc5Y7DvyIktd6wFINxXC+zmLXIWXhurp3yAjITBwEfWorS/Zly/bbp7xlISMCRIi/QWD5/Ig/YPKf9HQpAYdZvNKYJQjWmDMQ379618Pf6wjGJM3eML0RzDOrSAB8Hg8hEIhpJQkk8mx3jppjh5VHf9XrFhR9HOPxWyNYOzZs4eBgQFaW1tnVJABeDwCX736E16VlyTKuJtnIg2kTZ7f92tWuxYB4F1SMFzkLIywn7oZ6ONQGQLD5SJZyMxYcVmWo+5tW3LwhGqwlQcWFn7PW9YG6B1U41IbmtV9zBnl2QMDtMCYd0gpeeyxx4Y/7+zsJJvNlnBFpWcqEYxSpEhg+tIkqVSK06dP4/P5xt0yvRi0trYihKCrqwvTLG9n/0h+85vfADPvv3BoWKlKLmsNk8xQ+d63gbjEk8zzwr5fs8pUu3vDGrV2KUGGvTMyqMtpF54rNNvyZN1laY7t7JH0RiGUydN9wsIrJacDISoq3cMlqg0tKuqHhIpA+aVHQAuMecfhw4c5ceIE9fX1LF++HCnl8BPrfKVYAmM60hWjmTxh+kpVR3aknImW1w5+v5+mpiYsy6Krq2vGrjtVSuW/cFh1SQOGEERsi6HufEnWMB5OD0C88xDpmJ9ayyTp9rBgseqBIQTYAc+0Gjwdgj6BlECVunYo7yOXKS+Bkc1Jth+AuoAFWYuhY+oBcKhOdW7tK3TxbCxEMMq1RBW0wJh3OOmRG2+8kdWrVwPzO00ipZx0F0+Y/nbhMx3BcPwXM5kecZhtM0ls2+bJJ58ESicwVqxdQn9BCPZ1lKeZIJOT9A/YvLTzUVZXqMHcp6siuFwCadhYXheBChcB//Q/hft94BIQLDTbqjR9pBLllVra2yExLfAbFhKQJ88YPIHhNuENja1IKZGU3xRVBy0w5hlOeuSmm24a3lDns8AYHBwkGo0SDofP28THy3SmSS4mMIrdC8OJZs20nwBmnw9j165dDA4Osnjx4hltET6SZcua6S8024p2pTGM8noaBzjVr/pfvLjv16zybQYg26Q2S7IW+bCf+hnyEAR8IIGaZaoXRq3tIRkvH4ExGJfs74SGarATBsIFlQWDZ2S58l31RZXJs76xhbwBkSC43TpFoikxtm0P54xvvvnm4ZTAfBYYI9Mjk82hT6fAGM3kCXM7gjFbBMbI9EipSgRDEQ+DbpUaSQ4kyKbKy7AopeTACfBmouw48iyrWQiAf2nB4Gna5EJe6qtnZj1+r/J81K9SKcY6S3Cy49TMXPwi2Lakfb+kIqiG2dGdIe9y0ZRKYQMt6/wMxXsxzBzhcDWhighZA2rK1OAJWmDMK3bs2MHg4CBLlixh+fLlWmAwNf+Fw3SVqhqGwcDAAC6Xi/r6+rNemy4PRikjGLNtqupIg2epCPgYbrZlxXNky2w6aCwJ0QTsffHXGGaOVaZK5zStPTOT3fR7Z2yOhtstCPgg0urDAmosk45DJ2bk2hfj+GlJfwyqKgQybSJTJqeOm3ilpCcYIhR203nqIACLl64BUFNUy7REFbTAmFc4/oubbroJIYQWGEx+iupIpiuC0dfXB0B9ff15hsu5HMGYDR4M27Z56qmngNL5L0AJjExARTA8KYtcmTXbOtEn8QrJ8y/+kubgVYRti6jXR21TYWqqBAJuKoIXPE1RiQSBgIeYx4sLOHFoYFoM2hPBMXbWq+cG7KE8CIgezgAQrVXNyY53qSZ4i5evA9SI+8ppnN8yVbTAmEc4/oubb1ZGq6VLl+LxeDh58iSZTKaUSysZxYhgTJfAGKuCBKbHg2FZFh0dHcDMjh13mE0pkp07dzI0NMTSpUtZWphoWQq8HrArlagI5WRZVURYluRgJ1TaeV7Y+xirQjcA0Ft9xuBJ0I3wumakgsTBKVUdKjTbSnTnyeRm7vqjseeYxJbg8xbEQncagp4zBs8W5VnpPKkiGEuWKYEhKF+DJ2iBMW/I5XLDI6Vf/epXA6phk7OROE+u842pNNlycARGsVMkYxk8YXpSJF1dXeTzeZqamqioqCjaecfLSIFR6ifKi1Hq8lQHIQTBherPeGVekiij6aD9Mcib0PnKdvpj3awNKINnfmHhZytnkQ/7qKyYWZNiJAR54SLhU9c0ozaJEs5xcYydTvRCZi1k3AC/64zBc4UK8XR2ny0wJOVbogpaYMwbnn/+eTKZDBs3bjxrw5rPaZKRJarF8GAUO4IxlsETpidFUkr/BajvqaqqikwmQ39/f0nWMF4c/0WpBQZA00plmKw1LdJD5VMRceyUJOCRPP/kzwFYjfo5Di0r5EMMm1zQS0P1zK4rEhKYliAbUtufJ+NlqK80920oIXlqhyQSYngOix3NIYF8VrKwYPBsXaPCFJ096oFo8bJ1mJYk4BsR9ShDtMCYJ4z0X4xkPguM/v5+YrEYlZWVNDQ0TPo8050iuZDAKGaKpJT+C4fZ4MOwLKss/BcOq7c0YCCotEwGy6TZVi4v6TgNldLkhd2P4cLL0qzq09G8/ozhIu/xDD+5zxR+r2pOJQvNtoKGn57TMyswpJQcO2Xzi+clHvc5PoruLCLoputgDg+S08EKgmE3yXScgfhpvD4/Tc1Ly3oGiYMWGPOEc/0XDvNZYExliupISpEimYsRDJgdPoydO3cSi8VYtmzZ8HpLyaq1SxjwKBNwz5HyEBinB1UDqMzpQfZ3vszi8I0EpE2fP0BV3QiDZ8gz4ybFgE8JDP8CFfmptPwMnDZnLC1nmpKXDkie2QV11RAe0SJd5i1kNAcBN7EjBYNnneO/UAbPRUvW4Ha7y76CBLTAmBfE43FefPFF3G4311133VmvzedmW8XwX8AZAdDb21vUP1IXMnlOhwejHCIYs6FUtRzKU0fSurSJfrcyd/Z3pMiXQbOtgycgEpTsfe4ZLNtkW/1dAAwUdkTH4Gm7XYRnsIIEVATDllC9VDXbqrG8ZPtSM2L0TGUkj78sOdQFzfXg85wtrmQ0j5TKWyNPptTXWs4WGI7/wjChOlK+6RHQAmNe8NRTT2FZFpdffvnwk6+DjmBMXWBMV7twHcEoT8rF4OkQCrgYcqnIRaIvQXywtD6MRFr1c6iwLXbtfxaAVR7Vt8FsPjNB1Qx7iQRn3kPgL0QwqpepjbvedjNwokNNfJ1GTAseeV6SykBTrRj2XIxEns4g/CoaVTWgBEbl8oLBs6tg8CyUqApR3hUkoAXGvGCs9AioJ0av18upU6dIpVIzvbSSUiyBAWdEQHd395TP5aA9GOWHaZpl5b8AtWHGvUpgmLEc8YHSCoyTvRKXUL0cdh97AYBFWbUTRlYUBIZhkw35ZtzgCSo6UBGAyBJVflFnWvR0dTAUm77Iz0BMEk9BRRBqxog6SMNGDuQg6CaXsVmYSmEBLWsdg6f6ezVbKkhAC4x5wVgGT1Clqs4T63wrVS2mwHDOsWfPnimfy2Emq0ii0SiDg4OEQqFJz2QpBuUewdixYwfxeJwVK1awaNGiUi8HUCH/dKHZljdj0X+6dKWqti05eAKqw2CciLH3+Et4RIiWTEa1u157ZkfMeTw01pRmneEguCNeerw+vEj6uzP0dk+fMDt6SiIEBC8w0E3G8kgbhEvQdTCHG0lPqIJghYpojBQYtq1EXFBHMDSlpKenh927dxMMBrnyyitHPWY+pkmklMMejKl08XRoa2sDoL29fcrnAvWkPDAwgBDivDbhcEZgJBIJbHvqT14j0yOlmqsB5e/BKKfyVAeXS0BVodlWVtJ/snRGz8E4pLLgsywO7t9O3sjS1vqO4XbXFZVnOtLKgIdIqDQ/a5FCs63TAbUFGgNe+nqsaTF65g3JsW7wuC98nOzJIPxqPTGng2ed6uCZy2Y4PdiJy+WiZfEqcgZURyjp7+p4mBMC43e/+x1f+9rXGBwcLPVSyo7HH38cgGuvvRa/f3S5Ox8FRl9fH/F4nKqqqlE38Imybds2ALZv3z7lc4Fan5SShoYGPB7Pea+73W4qKiqQUhYltVUO/gtQ0Rqfz8fAwEBZpuwc/0W5GDwdQi1q96o0JOkeA9MsTeOo4z1SmShjeV45qtIjGyvVvRoqbJbSsMHvRnpcREIlWSaVIcjbgkShRDaYqMBMmtNi9Dw9KLEuEhyRpo3sy0Ko8Lt+QjXYki3qBp08eQhb2jS3rsDn86sS1XDx11ps5oTA+MAHPsAf/dEf8fLLL5d6KWXHhfwXDvNRYOzcuROY2hTVkTgRjO3btxflKehC/guHYvowysF/AeByuYajGOXmw8hms8P+i+uvv77EqzmblnVqI1qQN5Epg2QJpqpaluToKZUe4VSG3Z0vArDIbAZAthY6eOYtzEovIT/4fSWaQhsQ2BLcC1UvjIZ8CBEzpsXoeaCTiwupuDGcHrFtycKeKAD1GwsVJCcKM0gK/oucAbVlPEXVYU4IjHXr1E3fv39/iVdSXkgpL+i/cJiPAuPb3/42ALfddltRztfc3MyCBQuIRqPD0YCpMB6BUcxS1XKJYED5+jB+/vOfk0wm2bp1K62traVezllsvLyJqNtDyLYY7LeJlcDoOZhQlRIuy8YcyrD7iBIYzUkldqpWFupR8za5oK9k/gs4U0nSdEktAIvyblzJPNFkcYVZPKUqasIXSQXZvRmEV23HXYdy1Bh5ol4fratV1Pn4sP9irXqDhHCwvNMjMEcExtq16qbv27evxCspL1544QWOHz/OggUL2LJly5jHzTeBkUwm+d73vgfAvffeW5RzCiGKmia5kMHToZhGz3KJYED5+jC+853vAHDPPfeUeCXns3LNMjrcytwZP5VnsHfmjZ4neyVeD8iYwbHu/aQycZY3XEtzJk1OuGhdUzB4Ssi6PSWpIHEI+NQ6lrc1YQhBk2mS6B/kdE9xK0lO9ErcF9llpSWRPWfSI307VXqke0H1cCnriYLAcCIYUP4VJDDHBIaOYJzNN77xDQDe/va343KN/U/d2tqKz+fj9OnTJBKJGVpd6fjBD35AKpXiVa96VVEqSByKafSc6RSJjmBcmEQiwU9/+lMA3vKWt5R4NeezoKmWTlcUgGx3gr4uY0avb9sqPVJVoXo5OOmRVzW+E4ATtVX4g4W/QQJk0ENVuHRP4H6vKvP0V/s56VUb+8ljg0U1etq25ECnMmNekISBtCSiMPDNd1Q9MIhV6vdb2pLOXmVIX7JsnVrfLOiBAVpgzFmy2Szf/e53gYs/pbvd7uEnV6eyYi7zn//5n0DxohcOxYxgXKiLp0OxIhiGYdDZ2YkQoqSjxx3KsRfGj3/8Y7LZLNdee23ZlKeOJOCDfp8yuXsGs8S689j2zPkwhhJqcqoHiezPsqtDCYyVpvq7kl1a2CxNG3wupGfmO3iOxOtRVR22R3DKr8RYoiuPlbZIZ4tzjb6o8kqc263zXOz+zLC4yOVsFg1GAWjdpvwXVibPyT71ALBk2ToME8KBmZ1AO1nmhMBYunQpfr+frq6uefEEPh5+/OMfE4vF2Lp1K5dccslFj3ee5Oe6wOjs7OQ3v/kNfr+fN7/5zUU9dzGNnjPpwejs7MSyLFpbW8esNJpJnBRJR0dHaRcygnJOj4ASGNmqIQDqknlEwiBVpI1yPJwaUEO7iBnYts2ug88DsCyqUg71mwuP8TkLK+LD771wT4jpRghBOAiGy0U0qG6UZwhEwiCZKc41jpxS004vhJQS2Z2FChVFOb4zQ8C2ORWqoK5JGVBPd3dgmDkaFrQSqoiQyUPdDA+ImyxzQmC43W5Wr14N6CiGg/OUft99943r+Pniw/jWt76FlJLXv/71VFdXF/Xczc3NNDU1EYvFpty0bCIpkqkKjHLyX8CZn8X9+/fP2ACqCzEwMMCjjz6K2+3m7rvvLvVyRsXtFkSWWdhAUzaHkTSJRWdmJomUkqNdKj1i92Y4Fe1kKN7L6vrbqTdyxD3e4XHj5G1yJergeS7hIJjChV2jalPr0i5cSYOhxNR/5rI5SedpdU8uSMoEw0Z41Fac2K0ekAdbziiI46cLLcIL/otsDupmQQUJzBGBATpNMpJTp07xy1/+Eq/XO+4nrvkgMKSU05YecSiWD2MmPRjl5L8A5QmqqqpiYGBg+D6UkocffhjTNLn55ptpaGgo9XLGZNnqRXR7PHiQDJ62ZszoGUtCOgdeN8i+HLtP/g6AV9Wpvz1djdUjwvmCrNfDghJWkDhUFppt1S5WPURacha+TJ6ewakLjFP9yicx2ryRkdhD+bN24coT6nc5uO6MceNEj4oqLy5UkEjJjE+gnSxaYMxBHnzwQWzb5o477hh3E6n5IDCef/55Dh06RFNTE7fccsu0XKNYPoyZrCIptwiGEIKNGzcCsHv37hKvpvzTIw6r16ymw63i++nTefpOz0ypavdAoVIibYJps+uwSo+szhe8KitHPG5Lie1zl9Tg6RCpUL6RZRsXkHS5qbRMUkMWfb32lCJnUkr2n1AC5qL0ZCCg0iPxIYvWRAJDCJZtU2+WtuR479kzSEDNNJkNaIExx5BSDlePjDc9AvNDYDjRi7e//e2jdscsBsWIYJimSX9/P0KICz4xF8uDUW4RDGBYYOzatauk6+jq6uKpp57C7/fzhje8oaRruRjr1q3kmFTD9uyBHNHu/LSnmKSUHOmCygqwYwYI2HXweVx4WZlQLcubtxY6eJo2+F3gd5esg+dIAoUmX02rVtLhUemkvs7clI2esaT6LxS4sIiSOQsZN9Q9ATq3p3ABJ6oqh+ePkLc50e9EMFQFyWyYouowZwSGbral2L59O3v37qWhoYHf+73fG/f7WlpaCAQC9Pb2FnUEeLmQyWTGXVUzFZwIxksvvTTpGSH9/f1IKamvr7+gEJqrEQxg2JhcaoHx0EMPIaXk9ttvH77f5UpTax3dsgMAX38KO2YUrSJiLBJpSGYKm3Vflv5sL919HWyoewsVtkVPIEhjqzIrkrOxK3143OUxpMvphVFRV80Jt/I+xE8mIG1OqaPn8dPyonNHAGTcIJoaZM9hVXGT369+j9NLq88ckzXpPH0mgpE3VFfQ2VBBAnNIYDgmz0OHDmGapZsmWGqc6MXb3vY2vF7vuN/ncrmGh37NxSjGT37yE2KxGNu2bRt+Op4OFi5cSHNzM/F4fNIVOePxX0BxPBhSyrKMYDgCo9QpktmSHgEIRVykA10A1CcziIRBfJpbhp8eUE/U0rKRQzl2HVf+i6uq7wKgv7n6zMF5i3zQS0N1eQzp8jt/HoNu+rwDAIj+LO705I2epik51AU1F+t9AcjeLP/63T/nLz9xO7/b/TiN3VEAai45M2SkP9pNOpOgsqqW6poGsvnZ0SLcYc4IjIqKChYvXoxhGEVp1TwbyeVyw38QJ/OUPpcFxnSbO0cyVR/GeAVGMVIk/f39JBIJqqqqqK2tnfR5io0jAvfs2VOUabGT4fDhw7S3txMOh7n99ttLsoaJEPBBsDFPVrioM/Kk4xZD/dPrwzjardIjJEyQsOvQcwCszaufXd/aMzutlJD2eGmsntYljRu/FxBAwE2uQgmM2oRBIGvQG53cOfuiYJjguUiEQVqSgSOdbN//JACPPfoUDbksSbeHxRvOtOgc2cFTCEHWgPpZUqIKc0hggPZh/OxnP2NwcJDNmzdfsDX4WMxVH0Z3d/eEq2qmwlR9GN3dKo8+3gjGVARGuYxpP5fa2lqam5tJp9McO3asJGtwUmqvf/3rCQbL31UX9EHTkpUc96p/x+hpY1qNnsm0ZCih+lnY0Ry4BLsOPk/AVcfydBYLWLL1TJ2mAGy/m5pIefyceTwCnwcsCaFmZY5dmM3iShn0DcpJ+VcOnpTja+GdNHhqx0+xpRLPvt4WAE42VOMpNOaSUtLZc6aDJ4C0Z8cMEoc5JTDmuw/DSY9M9il9rjbbcqpqbr/99qKMZr8YU41gvPLKK8AZwTwWxUiRlKP/wqGURk8p5axKj4DaMJeuWM1xoX4esqfzDPaY02b07B2SDG91vVniVoyOrn1sqrkHr5ScjESIVCszwrDB0+cuaQfPcwmHVMShecUCej0efFLSd8rEnITRM5GWnOofX/WIPZjjqVf+B4BIRQ1b3Opn3V4xIv+Rt+kcUr+fjsAQYvZUkMAcExjzeehZT08PP//5z/F4PLztbW+b1DnmYgRjJnpfnMtUjZ5O5MOJhIxFsSMY5UYpjZ67du1i79691NXVTVtJ83SwbuMqOizVYl305zCiBtn89FzryCm1Qcu8hUya7OlUP7dXRm4GILG4+szBORs74sPlKq8NsjKkSlUXLltBh1vdqMGuPCJrTdjoebRLmTsvFgmUUtK3/yi7j72I1+Pnz9/2v9lsqLBH06UjOnPlbDr7zplBwuypIIE5KjDmYwTj29/+NpZl8Xu/93s0NjZO6hxzUWC89NJL7N69m7q6Ol772tfOyDWbmppoaWkhkUhM+F5alsVLL70EnBEqYxGJqPx2IpGYtE+hnCMYpTR6OtGLu+++e0Jm6VJzadsajuV2AlARTSPi+SlVRIyFLaE/ChUBVQ0hgV0Hlf9iXU6ZBCIbzpgVyVvkK7zUV5eHwdMhHIS8Ac1LV3JMKu9T/nQOV8ackNEzb0gOnIDacZg7yVg89cKPkVJyxaabWRS4iYht0e3xcCr1/PBh0rTp7FZdPBcvW0fOUH6X2VJBAnNYYJRDi+GZZKKtwUejubmZUChEf38/0Wi0OAsrMQ899BAAb33rW/H5LjIYoIhMNk1y8OBBkskkixcvvqhQdLvdhMPqj3gymZzUOss5glHKFMn3vvc9YPakRxwWNlXRL5Qga0ymkCmDWLz4JlnTBIQSC3Iwh/C42HXweWp8q1mcy5F1uViy+UyoQgKZMjJ4OkRCAsuC+tbFdFoqWuAdyBDMGfQMjf88J3olthzf5m9H8zy5U6VHrr/89cR2KwX4snuQnz/1zeHjkukoQ0O9BAIhGpsWkc3PnhbhDnNKYCxYsIDq6mqi0Si9vb2lXs6MsWPHDnbu3EldXR133HHHpM8jhJhzlSTPP6+eCGYqeuEwWaOnI0guFr1wmKoPo5wjGOvXr8flcnHw4EFyudyMXffo0aMcOXKEmpoarrnmmhm7bjEIByFS72XI7SFkWyQHLXqnweiZM9S1pJTIvixZd45Dna+wpVKNsj9RU43ff2Z7ERJMn5vayvJ6+g74AAGeoJeYR5mJa2NpfJk8/THG9aBq25I9x6A6fNFDAejZd5i9x7fj9wW5cvOthDqiAOzIv8AzL/2caLxfRS8G1e/moqVrcblcs65EFeaYwBBCzEsfxje/qVTvPffcM+Wn9LmUJplIuqHYTDaCMV7/hcNUSlWz2SxdXV14PJ6yHEEeDAZZuXIllmXNaNrzscceA+DVr341bvc4OiaVEQEftCxaxTGP6gUU7zboL7LAyOWlGhkeBDIW5CwOntyJZZlcFrwCgPzyMzuhNGwIuRG+8ujgORK/FxVeAdx1AxgIGvNZcjETM2uTGsdk1d6hEc3GLoI0bJ588vsAXLHpVjACLIrFsQC58ASmZfCrZx+CjMWJlBI8wy3CpYq4zCbGJTDa2to+1dbW9nRbW9u32trazktItrW1fbitrW1q052KxHzzYZimOZwvfsc73jHl880lgbF//35SqRRLliyZ8SFVkzV6TlRgTMXo6ZR/LlmyZNpap0+VUqRJHIFx8803z9g1i4UQguUrV9Ih+wDI9ebJRg1y+eKljE8XhoEJIbATeUCw55DqRrkxp9Iiw+PZATImstavKiDGU8I5g/gLEQyAptZWTnqVoOzuMHEZFnuPX7xcde9xOf7KmITBEzt+AsANl7+eYy+lhytubrtBRZ9//tS3sA2L4wNOBUmhmqwM79/FuKjAaGtr2wy0tLe3XwvsB+4+5/UIcMn0LG/izDeB8fjjj3P69GlWrVo17k3pQsylFMlEN+tismDBAlpbW0kmkxw8eHBc7zFNk5dffhmYeIpkMgKjnP0XDjNt9LRtm1//+tfA7BQYAOs3rua4qTYnb38GkSie0VNKyZ4OGA7s9OXA72b34RdYFLqeOtMg5vHSsupMJFWaEqPCR13lxaeLzjR+L7iESnM0L1pOh0t5meKnctT7LA6dgCNdYwuMaEJyeoBxR2ZO7t7PwZM7CfgruGLTzWT2qRblicXVXLHpFmqrGjlx+jB7jr1I56kzBk/bViXB4+qxUUaMJ4LxKuDRwse/AK4+5/W/AP69mIuaCvOtF8Z//dd/AWqAVzHc2XOpF0YpBcbI647Xh7F//37S6TTLli2jrq5uXO+ZigejnP0XDjMdwdixYweDg4MsWbKkrO/Lhdh4yRo6cupnriqWxpUwSKSLE8EYiMFQXG3K0pLIgRy2X7D38O/YEr4TgFMLqs8yOwog7fXSWAYj2s/F5RLUVkImD81LVnDcVq3W7d4cMmXSWAMv7oO+6Oj373CXxOcdf2XMk48p8/BVW27D7wtSdzIKQGRjBI/Hy23XvBWAn7/4bTo71R62dPl6cgZUhctPoF2M8cRFa4DuwscxYLifcFtbWxVwSXt7+8fH+iPe1tb2XuC9APfff/+015TX1Kif4t27d9PV1TWt1xovhmFMy1rS6TQ/+MEPALjpppuKcg1nw9q7dy8nTpzA5SqNTacY9+zZZ58FYOnSpSX5WXDE2pNPPsmNN9540eN/9atfAbBhw4Zxr9dJbRw/fpzLL798Qt+n09Crrq6ubH5XzsWppNm5c+e0rXHkz9r3v6/y41dddRWnTp2alutNN4sWVtCZegqr+n4aMxlarslgpE/R1TX1zSmZgS1LwOs2WBg+hbzO5NCxgyTTMbbVbAATKq4M0bJNRQKkBCQs8PcS8UI5/pgtrYVsHrIbqvhZ/ikQK6hIpGlZOQTBBC1V0HMasgkY+edQSggAG1vGdx2vy+CZQz8C4I33vAbTF2NhJk3K7eHqd1r4/Enua3wD3/nZv/H0K/9DPp/H4/Fw6dogLnc3Xk953j9QwzJHYzwCIwo4jp0qYHDEa38JfPFCb25vb38AeKDw6bTXjjY2NuL1eunq6qK6upqKioqLv2ma6erqGvMfYCp85zvfIZVKcdVVV3H11ecGliZHS0sLTU1NnD59mnw+X7KnuKneM8Mw2Lt3LwC33XbbsPCcSV796lfz6U9/mv3794/re3EiCtdee+24v/eFCxcCqmTV6/VO6J45M0+2bt06LT+fxaCpqQm/309XVxfhcHjY1FpMRv6s/e53aljX6173urK9JxfDtiUVlSFOeT0sMkza/0vQ/PsN3HHb1Do0JdOSp/ZKmupgYaSbroNVyMNxfrNzF14RZmPWDdjULKmma7uy6sm4gWgK0Fdfye9dIagKl98TeFef5Hc7JZHaEB2ZD0PoLuqiaTp/7cN7/QKEEAwlJBUBePVWMdzK+8Bxm90noalufN9T5tRT7D+wl1AwwvLg7bzyYJINQOeCGmp3K8+Kmw1cuuoaXj70W0AZdgdySzg9KLl8HbQ0z666jPGs9lnASUbeBjwz4rWVwN+1tbX9AljV1tb2t0Ve34Txer3DPoLx5r5nK056ZLKdO8di69atAMMVGLORvXv3ks1mWbFiRUnEBZzxUbz88stY1sWd/E4qZSIVL1NJkcwGD4bb7Wb9+vXA9PswstksTz/9NKDE4WzF5RK0tK7guEuVQCROGaSGLAxzas93x05L3CM7VfZmIOhh96EXuaTqHoLS5mRFmPrmM3UA0rSh2oeAsmoRPpJISLXgDkeqybiPk3S5qTQNYn0m5JVBuyYiGEzAS4eU6dOyJHuPT6xs9Bc/V70vrr70tfi8fvyHogCIddVnHfd7l791+OPFTgUJs2sGicNFBUZ7e/sOoKetre1pYAPw/ba2tv9XeO0P29vbX9Pe3v4a4FB7e/u/TOtqx8l88GH09vbyy1/+Eo/Hw5vf/OainnsuCIxS+y8AGhoaWLx4MalUigMHDlzwWMMw2LlTdWB07v94mKzJc/v27bNCYMDMGT2fffZZstksmzdvnnQ33HJh+cpVHCv4CazeHDJp0Ds0eYFhmJL9IzdUqaIT+F3sOfwilwVUCjC69GwxL4RqsLWgtnw7UFYECoPYbElz8zKOe9R96jluQMocPm5BDRw6AYdPSroHJNk8+Lzj+55k1uKRX/4UgOsvex3ppMWSoSg2sPTyM1F2adhcffntVFYrD9ZwBYmcfRUkMM4y1fb29g+2t7df297e/rb29vZ8e3v7H49yTOn+kp/DfOiF8dBDD2FZFq95zWuKXoKpBUbxGG8/DCfisnLlyglFXCbaB2Pfvn3cfffdtLW1kc1mWbdu3bBIKVdmyug5m8tTz2Xd+tV0GOrvn38wQ6WR56WDYFmTExmn+iXmyDHktkRKGIr30t3XweW2EmTVW0aMZzdt8LlICTfN4/MslwS3W1BTqZqHNTUvo0OoFp7p3jz20cRwmaoQggW1yvS58zBEJhCROfbSDg4dOUCkopptG27g6IspvFLSWVlJVd0Ip0LaxN9czd1v/QsA2q68VVWQCAjOohkkDrMroTNO5kOp6oMPPgio6pFiM1JgzNaW6+UiMMZbSTLZ9Y43gnH8+HHe9a53sXHjRr7//e8TCAT44Ac/OJwSKGdmaujZnBIY61bRkVGzQeoTKXxZk0RK0tkz8d9nKSW7j6oqhmFMG+EW7D70IotC19NkmsQ9XpZcMmLXzViI+gBSUnYdPM+lsQrSWWhevJzjVgcArr4sMmZAzBg+zuNWVSfRJITH2fRK2pInfvZdAK6+9Ha8Hh/53SqlmVxx9sOEtCSuGh9vf/ff8uPf9LN523XkDKiOzL4KEtACY1Zy6NAhXnjhBcLhMHfeeWfRz7948WLq6uro7+/n5MmTRT//dJPL5SaVbpgOLrvsMkBtXhcSa1MVGBfyYHzuc59j9erV/Md//Acul4v3ve99HD58mH/9138ddzlsKRmZIpkuwTs0NER7ezter5drr712Wq4xk2xct4qeTDsZ4aLGyBMfMqn127x8iAl7MfpjEEtBKHBmg5OmhKCbPYdf4LLwGwE42VQzbIAEFe4XNT6Q54iTMqS+WqipqotW0JHdAUBkKA0BF3ZH4qxjg35BS8P4N3sZzfHE9h8DcP3lr8OyJC3dKkrSsG1Ex1OpIhWEPQghqKxSBZuzcQaJw5wWGAcPHhyXuW624UQv3vjGNxIKFb/3rhBiVqdJdu/ejWEYrFmzpuTh/+uvv576+nr27t17wSjGZAXGxVIkyWSSD37wg+Tzed761reyb98+vvSlL82qConm5maqq6sZHByku7v74m+YBL/5zW+QUvKqV72qLCrPpsratSuR2BwveAR6jpt4Y1lyhvIQTIT9x+VZDZ5k1gJbInxu9hx6kctcawDwrD+7wkcAWZ+Hmkrwesr76TscVH6RhS3LOJ5+CoDGVArT50YO5JGxyc+8P/rcdk70Hqa6uoZL115L594s1UaeAZ+f1tUjRjvkbUSFB+E7uz193tACo6yIRCK0tLSQy+Xo6Ogo9XKKipTyrOZa08VsFhjlkh4B8Pl8w/9O//Ef/zHqMU7ERQjBpZdeOqHzXyxF8tJLL2FZFpdeeikPPvjgcIXVbEIIMe1Gz7mUHgGoqKigvr6ZDhEFINlnII8lqQvZ7DoKmdz4REYyLTnZC1UjNJc9pAbPZXNpurr62ZAHC8Gyy8+EKYb9F9JNc33Rvq1pw6lwWdiyjKTZRZ/Hg1/a9HUa4HNhd6YmdV6ZNtn+O/WzdcN1r8bj8TL4kvpdPd1Se3baI2NBw/lOTinHn44pN+akwIC5myZ54YUXOHLkCAsXLhxX86bJ4giMiQ7rKgccgeGkJ0rNu971LkD1Lclkzp+e5ERcVq9ePeGIy8UExosvqhkRl19++YTOW25Mt9HTERjT3QhwJlm+YiUd1nH1yekM0pS4B7IAHOgcn8A42n12aaqUEnk8CS7BgY6X2RK5BzeS49VVhKtHPHlnLES9H9MWNFSV/+bo8QiqKqCydjEul4ujbiWievekIexB9maQSeMiZzkf+3SGV46qic6Xt10JQGVhemrgkrN/16UtcVWPMqxyFs4gcdACY5bhpEfuueeeaZ30qCMYxeOSSy5h27ZtRKNRfvSjH533+lTWezEPxlwRGNNp9Dx58iSHDh2iqqpqxqfuTierVq+iI6e8SOHBNCLiRR5LUlths6+Di7YPN0zJgc5zej3EDUhbCBfsOfQil/vUppleUX3We6VhI2oDs8J/4dBYA6bto6FxEc+b6u+eb9eAEldeN3ZnckLnk6aN1Zlg19EXALhs25UM9hgsTiTICRcrLhtRnjrsvzh7lqhtS1yztIIE5rDAmIu9MAzD4LvfVW7k6UyPgOqNUFlZSXd397TlvaeDTCbDrl27cLlcbNmypdTLGead73wnMHqaZCoCIxJRZYHJZHJUv5EjMK644ooJn7ucmM4UyW9/q7om3njjjWU7VXYyrF2zio70k4DyE9guoTb+vhw+H+w5emGBcbJXYlojSlMBuysFXrVt7D7UzjZDqYemyyJnvVcgyfvcVASVKXI20FAtyBkqTfLM4P/FEIJlQ1EGThsQ8SBPZ5Ej+mJcDHsgx9GTe0mkozTUttDS0krnC0qkHK+vxh8csf3mbESlF+E9e0vOGVATGf+sk3JjzgqMuRjBePTRR+nv72f9+vXTvnm6XK5hP4Az4XM2sHPnTizLYv369WVl1rvnnnvw+/089thjdHZ2nvWak4aajMBwuVzDIiOVOjtP3NPTw/HjxwmHw8O/D7OVDRs2ALBnz56iG7edUt254r9wWL9uFQmjk0GPl6Bt0XvCKEQxEtSEbI52w2D8fJFhmJJjp2y2H4DqEdEHmbWQp7NQ4cG2bfLdTVTZFr3+AAuXj5yeaoPXTVq4aZ1F/coiIWVMXdi6jJTVzd6qIC7g+JMxtcF7BPbJ8XkxpJRwPMWuE0rgb15zNUIIXAdUpNFcXX32G7LmqP6LTA7qit8df8aY8wJjLjXbKvbk1IsxG9Mk5ZYecaitreX1r389Ukq++c1vDn89m81OOeLipEkSibPL6Zy5Gm1tbdOaTpsJampqaG1tJZvNDncgLQa2bQ9HMOaawHCG7R30qgqI7hcTCK8LadjIvhyhAOw8LIdLf9NZyZ5jNj/+reT5PZwXfbB7MyBAuARHjh5mk+d6AHoXnWNWzCr/Rc4ULKiZPU/eZ4yeqrPtoSrlX6ncN1A4wIvsSiMz44hiJExk0hj2X2xe8ypyKZvFfao8teWyc/JGNriqzvdf5A2ojZz35VnDnBUYzc3NRCIRBgYG6O/vL/VypoyUkl/84hcAvOUtb5mRa2qBUVxGpklsW804eOWVVzBNk3Xr1hEOTy5ZPZbAmCv+C4fpMHru3r2bgYEBWltbWb16ddHOWw44gwqfyP0GgMg+9XdQVHiQRxNUBeFUPxw9BS/sUcJi91FVMbKwXpwlLqQlkR0piCiPwMs72rlMLgagYtM5ZsW8DbVq/khV+QQRL4rXI4iEoGHBUgCOGD8k7XKzKJng1JEcwiXAJVSa6CLYp9LYLsmuA6rZ2aY1r+KVX5oEbYuuUAWNrSMiPrZUoZPw6Om52TiDxGHOCgwhxJxKkxw+fJhoNEpTUxPLli2bkWtqgVFcbr75ZlpbWzl69OhwWL4Y63V6YSSTZ5vQ5prAmA6j569+9StA/dvM1jz3WIRCIRY2t/LC4JdJudwsTiToOpxD+NzIrI3dl6E6As/vkXT1q1kbC2rFqD0r5FAOaVgIj9oydj53muWGScblYtm2s3vxCMAOevF5VRRkNtFQA9UNKoLRPXSIY62qxrb7t1F1QKUXeSKteoGMgcxbyO40xxNHiSUHqKtuorlxGf1PpgEYOmdeCzkLUe1HuEffjkOztIIE5rDAgLnlw3DC3ZdddtmM/SFcs2YNoVCI48ePMzAwMCPXnArJZJJ9+/bh8XjYtGlTqZdzHm63m3vvvRc4Y/YshsAYLYIhpZyzAqOYRs+51v/iXFauXEXejrO/QYmA009HARBhD/JokpAXWhoEtZXigq2o5fEUInjmCVscVv1UjtZE8PvPbCPSssHjIi3cLKybfebExmqoqS8IjMFOKi5X3TQbD/armSAuAQLskynkGHNd7F5VCrzroIpebF57tWqXfjAKQPWWc0rRsxY0nF8mYtmqRHi2VpDAPBEYc8GHMVJgzBRut3vYFzAbjJ47duzAtm02btxIMFiej0733XcfAN/73vdIJBKTGtF+LqMJjMOHDzM0NERTUxOtra2TX3AZUewUSS6X46mnVNfGm266qSjnLDfWrlFC4FSdMhYvONynNkq/G5kxsfuzFz2HTBrIaA4RUgJjKNbLxpw6r1h/Tqv5rIWo85PJCxaWfxf684iEBJXVDQSCFSQzMRpXZxny+mjMZejYVbhXER/28STWc71YncmzPBnSLvQJCXvZeeBZADatfhXdR/M0ZLMk3F6WbDrnb5MEV+X5/otcfnZXkMA8ERhzLYIxk8ymhlvlnB5xWLlyJddeey3pdJpvfOMb7NmzB7fbzebNmyd9TidFMlJgjIxezOY/UCNZt24dbrebQ4cOjdqwbKI8//zzpNNp1qxZQ1NTUxFWWH44Rs+T1q8Y8PppyGU5tkPdOxH2Io8mlAfgAtjd6eHSVICde7ezOa/ExpIrz3Eg5myo94GA6vDs+7mLhAAhWNii0tC9p4/RtUxNqx56Thk0hVvgqguA3408ksB6rg9r1xByKAdDeWTWBo/glYNKYGxe8ypO/079bp5YeP68FvyuUf0Xs3kGicO8EBizPYJhmuawD2KmN8/Z5MOYDQIDznT2/NjHPoZt22zYsGFKM2WcCMZID8ZcS48ABAIBNm7ciG3bfOUrX5ny+X7yk58AcN111035XOWKIzC6+o/SvUJtlLHnCxul341Mm8iBsaMYMm8jT6ah4kwDqM6XDfzS5mgwQO0C7/nvCXlxCaicRQZPB59XUBGAJctUWfQvnvkOtVdVA7Coow/TOCPGhNeFqPFDjQ8Zy2O9NIi5YwARcHPi9GGi8T5qKhtpbVpJ6EgUAPe6c2pOUyaiOaRSL+eQN6BuFnRBvRBzWmCsXLkSt9tNR0cH2ezFQ4Hlyt69e8lkMixbtoz6+plt7K8FRvG5++67qaioGPa1THW9o6VI5kqDrXP5p3/6JwD+7u/+jq6urkmfx7ZtHnroIQDuuOOOoqytHHEERvdABw1XVwOw+Hgf+ZyqYhJhL/buqAr1W/Z577f7s0ipntodantV7qNn4dniQuYtCLrJuNw01szO8eKgOnq+/q1/g9vt4afPf5OMbw+nAyGqTIPDvzu/gkQIgQh7EXV+RJUPUeFh535V+rx57atIxWyWRGNYCJZfcW55qsRVP7aLczZV4YzGnBYYPp+PFStWIKXk0KFDpV7OpClVegRg/fr1+Hw+Dh8+fMGR4KUmHo9z4MABfD7fcK6+XAmHw7z5zW8e/rzYAiOfzw97ZspdbE2Uu+66i9e97nUkEgk+8IEPTPo8v/3tb+nq6mLJkiXDInousmLFCoQQ9EZPsHAJdIUqiFgmh59TG6XwuyHixT4Ux3q+D7svM9wXw5k7IirOhO+z2TSbs2rXW3BZw9kXS5uIlhCpnKB5FvovHBqqYcGiS3jT2z+AlJIv/Ndf0bdKfUOZFwcv+F6nyuYVx3+x5lUc/MUQbiSd9ZVnzWuRhg0B96jpkVxeTbCdjVGgkcxpgQFzw4dRynC31+sdrsjYsWPHjF9/vDgRlk2bNuH3l7/t2kmTwNRFwLllqrt27SKXy7FmzRqqq6undO5y5Atf+AKhUIjvfe97PPLII5M6h9Ny/w/+4A/mjEdlNAKBAK2LFmHbFqdPHmNonYqAGtvPbJTC41KeAo8La+cQ9o5BZMKAaF7NHfGf2RRf/O1BGk2TIbeH1VvPKbe0wVXrBzm7Q/tVFQIkvOO9/0DjgsUc7trN0QoVkVh2aoBc5vxIz0iklMMCY/XCa1jy8kkAwm86R3UlDURraNSfv1gKVrTMboMnaIExKyhlBANmR5qk3CaoXoyrr76aa665hpUrV065pPbcCMZc9F+MZPHixXzsYx8D4P3vf/+EDZ+GYfC9730PUC3c5zqXFQTs49t/xKLrqrCBZacHSMbP7kgp/G5c9QFkysR6oQ9rX0wZEAtIKcn/VnUF3V8ncY9Im8icBSE3MuSGWeq/cAgHQQLBYAV/8eEvAvCtX/9/dITDBG2Lg08nLvj+rt6jDMR6qI7Uk/ptJWHL5Gh1NZfdfY5fRRYE2SjYEprrZ7e4AC0wyp6RraRLFcqdDQJjZFvs2YAQgieeeIKDBw9OOeJyrslzrgsMgL/4i79g06ZNHDt2jH/5l3+Z0Hsff/xx+vv7Wbt2bVn2Syk2999/PwA/ef6bVIVydFRX45c2h58cfaMUYS/U+sGSMCI98qtHnufqpAsDwZb/75whI2kT0VJBNi+oq2TUZl2zhYBfEPSrmSyvuv5Orrn6LjK5FNsDJwCQOy6cJtm5/xkA2la+mVUHTgHgv6vlLE+KI8hG3l+HvCkJ+M6eAzNb0QKjzNmxY8eUW0lPFadHQzkLjNli8ByJ2+0uSgjUSZHE43EAXnhBjYeeywLD6/Xy5S9/GYB//dd/nVCl2He+8x1ARS9mewh6PNxwww1csmkLsUQfjz3/MLlNKlTv2zV28zzhEoiId/j+5I0clc/6cAEvNvnYcMU51RASXHV+Ullonlkf+rTQUA3pnPr4/g9/noAvxA87/hELWN43SHxo7E6eTnrk6tRb8Eub/QsbWH5u74tMoXpktPRIEpY3z/70CMwDgbFmzRpACQxn/sNsotTpEVANjjweD/v37z9vYmc50NPTw9GjRwkEAqxfv77Uy5lxRkYwYrEY+/fvx+v1Tqm3xmzgqquu4r3vfS+GYfAnf/Inw+bEC5HNZvnhD38IKP/FfEAIwYc++P8D4OGnvsLyqyrICxfLBqMMdBvjOscP/+sJLs1KUi43W95x9swWmbMQYQ8i5MGyoH4W+y8cGqtVHwqABS1LuO+ev2Uof5BdARdeKTnyVHzU9zn+iyUVN7GtP4OJoPGNC88/0JbK9zIKlgUtcyA9AvNAYNTW1tLY2Eg6nZ5SWVupKAeBEQgE2LBhA7Zt88orr5RsHWPx9a9/HVDtnj2e0QcGzWVGejC2b9+OlJItW7bMCrPrVPnEJz5BQ0MDTz75JN/61rcuevwjjzxCPB5n69atc2642YV485vfTNPCZk70HGTfoV/z/2/v3sOjqs9Fj3/X3HInJCGQECANIaDIReAHFFFEkLugcBCkomItUtQW3Vh39949u6d1t2f3sVqrtgKnuIuim6IiKCJRg6IWtvhTykWuIkEM91sIIbeZrPPHmokhF5KQSdZk5v08D09IZmatN+8zk3nnd3nX152ScQD5Hza8M+zU2WP02d8JgG3ZsSR3rPG8uuiFzrH4Kk0Mw+o+2dYlxhuY1T6P/q85j9A9vTcbyq3pj/b6GGdP1r6q6tGThzh59gj3Jj6KE9iTnUb69y7t0mmW+TDi3Jfszgmo8Jq4XeGRQ4iAAgPa9jRJKBQYELodPb1eL88//zzw3VxzpKleYETC+ovqkpOTefLJJwFYuHAhZ85cfn48MD0SKaMXAR6Ph4cX/BSAlR8swTnQusZG+z0NX2l67V81Pcq9nHK5GXZXj9p38E+PnCuCrM5Ws6q2LqFG3ztXYjQP3/Mkfz/7J466XHQuKebC03s5+nX5JffbvncTfRJnM7jUoMThpPv0TrUPftELGXU31isstqZH2moPkZqkwAhhgd4O1beK2iVUF3quWbOGw4cP07NnT8aMGWN3OLaIj4/HMAwuXrzI5s3WBZbCrcHW5cyePZuRI0dy6tQpFixYUO9USVFREWvXrgVg5syZrRliSLj//vuJjYtj24GPcaUe5ILTRZfiCxzeW38Twi/3beWm49a1bPIHJhEd67zkdrPUh9HOjRHjoqwCsjuHxxtjtAfcLvBWu6DZNSNHMHrweH527gG+inLTsawE15LdHPjHxar7/GPP3/lhzN0AHOiTQVJqHSOq/oKsLl4fdOkYHjkEKTBCWmC4u3///rYPd4dqgfHss9Y2soceegiHIyKezrU4HA4SEqwx1Q8//BCInBEMsNYYLFq0iJiYGJYvX141ZVbTm2++SUlJCcOHD6dbt26tHKX9kpKS+OG99wKw6sNFHOpmNco6+f5pfHVcGdQ0TT5bWUCa18vhqCiGTuta+6AlXkiPpaTMJCEWktv4tTMCDMOgYxIUV6u9jOQo7pv8r8Ql+fjn4zP4LMqknbeC1Jf38OUGa0eO8c3V9Cqv5JzLTe9ptbuNVS/IavL5TFxOSA6T6RGQAiOkhcr0CED//v0xDIMvv/wyZNqub9++nY0bNxIfH191GfRIFZgmOX/+PImJiVUtoiNFr169Lpkq27ZtW637BJprRULvi/o8/PDDGIbBxu2rcfa1/vxfc/Ao+3/7Fd/uu/R1/f7GdUw6nwFA8ejOl1ykC6wCxGqu5aGwGK7ODI+dDwE5XQyKq7VYMVwO2uV05k8Pv8OIYWP49dEprI8qIsqspPu6vWxe/i23V1wPQMGQLsTEO2sf1L9epS6FxZDZiUv6i7R1UmCEsECBEQqfRuPi4rjqqqvwer3s3LnT7nAAeO655wDrEuiBN9hIVf33Hzx4cESO5txzzz3cd999lJaWcvvtt1dt2wU4c+YMubm5OBwOpk+fbmOU9srOzua226bi81Xwaf4S9o3N4ZzLQ1ZhIUlLvuTzpccoveijtOwipze4aVfpY298LH1HJtc+WKkPo70bM8qJaUJGavi8MQJ0SrYWW14o+W50x5EWQ6w7jsfue45/mfcnlp67jxfdB3EAA/9xjAyvlwKPh76Tk2odzzSxpkeSal+aHayLm3XrFF45jIi/Qt26dSM6OpojR45c8kcn1IXSCAaE1kLPM2fOsHz5ciByF3dWF+iFAaFRkNrl2WefpX///uzfv5/77ruvaj3GqlWrqKioYPTo0XTqVMfCuwiycOE/AbDu05fIvsFJ3M+v4cusdByY9Nn9Lad+s5vVizczscRqaBF7W2adiw7NEh90jrU+eadBTFR4vTkahkG/bKsvRdXP4t0YCW7MUh83DZ3G//vVRnYm/JUnHZvwYv3+W7ufxe2u463VBKN9PdMj/h04bf3y7DVFRIHhdDqrtqTt3bvX5mga58SJExw6dKhq5CAUDBgwAKDO4efWtnTpUkpKShg3blxVr5NIVn0EI5ILjJiYGF599VUSEhJ47bXXqtboROrukbpcd911DBkylAsl51j//ivEt3cy8MEMCu7szeGYGFLLSpn6TSJRZiXbU9vR49rafb9NEwzAkRRFSRn0yAiv4iIgPcWgfTxcLK22RqVbLOZFa4tqpw5deeqf19D1+jIWVjzHb3iP/jfXMdoDVv/venaPnPdPj9SchmrrIqLAgEsbbrUFgdGLQYMG4XTWMZdng8BOFrt7Yfh8Pv785z8D8JOf/MTWWEKFFBjfycnJqVro+eijj7JmzRo++OAD3G4306ZNszk6+xmGUTWKsWrjEnyVPrbsyOOpt6bw4JGxLHXtpdRwUGo46DLze3UfxASjvYcKp4PYaOiQWPfd2jqHwxrFOFdtFMOREo0R5cAssYoMp9PFXVN+xsJ/eoSb7+hDj+/1rXWcwEiaI6nuxfpl5ZCZFl7FBUDEdCVqa+swQm16BKjqDLl9+3YqKyttm+dfu3Yt+fn5ZGdnM2HCBFtiCDWBKZIuXbqQnl5H58AIM336dH7605/yzDPPMG3aNEzTZMKECWF5ddkrMW3aNDIzMzl0KJ8f/dv1HD6+H4DkxI50HlNCRZ+rML0u0jPqXi9gfRqP4WwR9O8RPn0b6pKRapAQa1JSZhITZWC4HDj7JuPTpzDdjqpLtOdk9iMns552AkUVGC4Dw6j9YbHSPz3SoX0L/hI2iZgRDCkwmq9jx46kpaVRVFREfn6+bXEEhr0ffPDBiFzMWJfACEakj15U98QTTzBkyJCqSwRE8u6RmlwuFwsWLADg8PH9JMQlMff2X/Lif2puG/0jOnSKJbWe4iLwadxI9FBZCV3DqG9DXRwOg/494Gy1a8MZ7dwYVydinitvsEW9WVQBbgdE1z0SXXQRMlLb9gXi6iMjGCHINM2QLDDAmiY5duwY27dvp3v37q1+/l27dpGXl0dsbCz3+vf0C6rWGI0dO9bmSEKHx+Nh5cqVKKUwDIPJkyfbHVJImTdvHl/u3EPp8SRuGXIvqV0beZWyC14Ml0HRRSedO0BcTPi9MdaU0cEgLsaktNwk2mP9vo60GDhfgVlw0br6bB3MCxXgNHBemwzGiTrvc7EU1FXhmcOI+fgX+AO8f/9+vN7aPeRDyTfffMPJkydJSUkhKyvL7nAuEZgmsWuhZ2Br6t133y3D3dXcf//9bNiwgblz59odSkjJzMxk165dbN++nbi42osVI1lsbCx/WbqYZ5f9B+7oRMrOljf4GPNChfWuEeXkYgn07Bqeb4w1OZ0G/brD2WqbEA3DwNEjASPBZY1S1GAWV4ABzgHJde4cAaxpl2joWHtXa1iImAIjPj6erl27UlFRYevwfmMERi8Cn7xCiZ0FRmFhIS+++CIgiztrMgyDnj17ypRRHVJTU0lLS7M7jJCVlOLiuh+kcOEiVNTxRhlgFpVbn8YHpmAaBh53+L4x1qVrR4PoKCgr/25KxHA6cPRJgkoTs/y7S7ibxV4wwTkgpd7iAuDMeRjUMzynRyCCCgxoO9MkoTo9AvYWGMuWLaO4uJhRo0ZF5GXZhWgp6V3cDJyeTOHZSnwltUd4zcJyjGhn1RumrxJ6dguvrpMNcbkM+mdbRUF1RowLxzXtremSStPawuqrtHIVW39xUVhs0jEpvK49UpMUGCEolDp41tSrVy88Hg9ff/01RUVFDT8gSEzTZNGiRQA88MADrXZeISJFj6uiyJmUzLnjXiorqn0aP1eOEe/G0T8Fw79Q0TQhM8y6TjZGt04GUR4or7h0YaejQzRGdgLmqVLwVuIcmFLn5dgDTNPkQgkM7GmE3Ch1MEmBEWIqKyurOmWG4giG2+2uGj3YsWNHq51348aN7N69m86dOzNlypRWO68QkeTagVGk3ZjE+SPlmN5KKs+WYSR5cPRPwnQZXLhocuSUidsF7eLC942xPi6XQd/ucLqOhtCOzHgc3ROs4iLefdnjnCqEHhmQkhjeOZQCI8Ts27eP8+fP06VLl5CdN7ZjmiTQWGvu3Lm43Zd/8QohrozDYTBsRAxxA5O4UFBGeftoTmckcqzQ4OQ5iI2BwVdBXLTdkdrne+kG7eLg3IVLRzEMh4GzR7sGiwuvz8Tngz5Z4V1cQARtU4W2UWBs3rwZCM3Ri4DW7uh59OhR3njjDZxOp+ySEKKFuV0GI8bG8qHHiSvJTb80gw6JVsvsQCvrggKbg7SR22VwY3/I3WJSXGoSF920QuHkOas5WSRs742oAiM9PZ2EhAROnz7NqVOn6NChkfu+W1Hgmgmh3M+gtUcwli5ditfrZerUqWRkZLTKOYWIZLHRBhPHR/AwRQPiYw1GDoB3t5i4nSYed+OKhZIyk2iPdSn4SBBRUySGYYT0KMa3337L+++/T1RUFDNnzrQ7nHpVH8EIdElsKT6fjyVLlgAwf/78Fj2XEEI0VkqiwfX9rBEJn+/y3TwDzpwH1St8t6XWFFEFBoT2NMny5csxTZMpU6aQlBS6G8xTU1NJT0+nuLiYgwcPtui53n77bQ4fPkyPHj0YPXp0i55LCCGaomsnBwN7wtEzNNgy3FcJqe2ta5tECikwQoRpmixbtgywulSGutaaJgks7pw/f740kRJChJyrMg16dYNjZ+q/T2WlSWUlDOplhPWF4WqKuL/YoVpgfPbZZ+zZs4eOHTsybtw4u8NpUGsUGAcOHCA3N5fo6GjmzJnTYucRQogrZRgGA3MM0pPh5FmTsnKTwgsmx8+aHDtt/Tt5DqI94b8ttaaIWuQJoVtgBEYv7rzzzjaxDbM1CozFixcDMHPmTJKTk1vsPEII0RxOp8GwPvDhP0wqKqBTMnRIhIRYg7hoiI2Go0ftjrL1NarAUEr9DrgOyAd+qLWu8P98MvALoAL4XGu9oIXiDJrs7GycTicHDx6ktLSU6Gj7V0qXlZWxYsUKAO655x6bo2mclt6qWlpaygsvvADI4k4hROiL8hiMGxJZIxQNaXCKRCnVH8jQWt8A7AGmV7t5GzBca3090FEppVomzOCJioqie/fuVFZW8tVXX9kdDmAtZDxz5gz9+/evGhkIdb169SIqKoqDBw9y/nwdbe2a6e233+b06dMMGDAgJFumCyGEuLzGrMG4DnjX///1wPDADVrrb7TWgSvjlAMtu2cxSEJtmiQwPdJWRi8AXC4X11xzDdAyoxiBq6bOnz8/rHv1CyFEuGrMFEkSEJg9KgRqTYYrpQYDHbXWX9Rx2/3A/QAPPfQQY8aMufJogyTQrGnLli0MGzasxc9XUVFBQT2t706fPs26detwOp3cdNNN9d4vFPXo0YMvvviCjz76iKysrKAdd9euXXz++eckJCQwcuTINpUTO13ueSbqJ3lrOslZ04VzzuprgNiYAuMc0M7//0Tgks04SqkuwNPA1LoerLVeAizxf9u4biQtbPDgwSxatIgjR460SmfIgoKCes/z2muv4fV6mTRpEtdee22LxxJMw4YNY+XKlRw6dCioeXz88ccBmDNnDjk5OUE7bri73PNM1E/y1nSSs6aLxJw1ZopkE3Cz///jgL8HblBKJQArgHla6xPBD69lhNIUSVucHgloiZ0k1Re8zps3L2jHFUII0boaLDC01v8AjiulPgauAV5XSi323/wwkAU8p5T6UCl1Y0sFGky9evUCrAKjoe5rLWnHjh1s3bqV9u3bM3nyZNviuFKBnSQ7duwIWsvw3NxcCgsL6d27d9UaDyGEEG1Po7apaq1/VuNH8/w/fxx4PNhBtbSUlBRSU1M5efIkBQUFdOnSxZY4AqMXd9xxR0hsl22qlJQUMjIyKCgo4MCBA0GZzghc7O3WW29t9rGEEELYJ+I6eQbYPU3i9XpZvnw50DanRwKCOU1SXFzMm2++CcCUKVOafTwhhBD2idgCo2/fvgBs3rzZlvO/9957HD9+nJycHIYOHWpLDMEQzALjrbfe4uLFiwwbNoyuXbs2+3hCCCHsE7EFRmC7bG5uri3nf+mllwDrwmZtuc9DMDt6BhZ33nHHHc0+lhBCCHtFbIExatQoXC4Xmzdv5uzZs6167qKiIlavXg1Y1x5py4I1gnHu3DneeecdHA4HM2bMCEZoQgghbBSxBUa7du0YPnw4lZWV5OXlteq5V61aRUlJCddff31QG1TZIScnh+joaA4dOsS5c+eu+DhvvPEG5eXljBw5krS0tOAFKIQQwhYRW2AAjB8/HoB33nmnVc8bWNx51113tep5W0L1luE7duy44uMEdo/MmjUrKHEJIYSwV0QXGBMmTABg/fr1rdYP48iRI+Tl5eHxeLj99ttb5ZwtrbnTJCdOnCAvLw+32820adOCGZoQQgibNKoPRrjq168faWlpHDlyhJ07d1btLGlJr7zyCqZpcsstt5CUlNTi52sNgQJj9erVpKenk5KSQocOHejQoQMpKSm43e7LPv7VV1+lsrKSiRMnkpxc61I3Qggh2qCIHsEwDKNqmmT9+vWtcs7A9Mjs2bNb5XytYeDAgQDk5eUxffp0brrpJvr27Ut6ejoej4fs7Gw+/fTTeh8vu0eEECL8RHSBAbRqgbFjxw62bdtGUlISEydObPHztZbhw4fz9NNPM3fuXKZOncoNN9zA1VdfTWpqKg6Hg6+//ppJkyaxb9++Wo/95ptv+OSTT4iOjpbmWkIIEUYieooErH4YDoeDjz/+mAsXLhAfH99i5wqMXsyYMYOoqKgWO09rMwyDBQsW1HlbRUUFt912G+vWrWPcuHFs2rSJ9PT0qttXrlwJwOTJk0lISGiVeIUQQrS8iB/BSE5OZujQoVRUVLBhw4YWO4/P5+Pll18GwmP3SGO53W5WrlzJkCFDyM/PZ8KECZw/f77q9sDuEZkeEUKI8BLxBQa0zjTJxo0bKSgoICsri+uuu67FzhOK4uLiWLt2LTk5OWzbto1p06ZRVlbGvn37+OKLL0hISAirKSMhhBBSYACX9sNoqe2qgdbgs2fPbtOtwa9Uamoqubm5pKWlkZeXx5w5c3jllVcAmDp1apu8mqwQQoj6RfwaDIBBgwaRkpJCfn4++/fvp2fPnkE9fklJCa+//jrQ9luDN0dWVhbr1q3jxhtvZMWKFbhc1tNPmmsJIUT4kREMwOl0MnbsWKBlpkneffddioqKGDx4ML169Qr68duSAQMGsGrVKtxuN16vl5SUFEaPHm13WEIIIYJMCgy/6l09g23VqlVAZC3uvJybb76ZZcuW4Xa7mTdvXoONuIQQQrQ9Rmu1yPZr1ZM1xfHjx0lLSyM6OpozZ84QExMTlOOeOHGCzp07A1ab8I4dOwbluOGguLiY2NjYOtekFBQUkJGRYUNUbZfk7MpI3ppOctZ0YZ6zOhcWygiGX6dOnRg4cCClpaV89NFHQTvu3/72N3w+H+PHj5fiooa4uLiIXPAqhBCRQAqMaoK9XbW4uJjnn38eCK/W4EIIIURDpMCoJpgFhs/nY9asWezevZvMzExuvfXWZh9TCCGEaCukwKjm+9//Pu3atWPPnj3k5+c361iPPPIIb731FklJSbz44otBW9MhhBBCtAVSYFTjdrsZM2YMALm5uVd8nD/+8Y88++yzeDweVq9eTXZ2drBCFEIIIdoEKTBqCEyTPP7442zatKnJj1+zZg2PPPIIAC+88AIjRowIanxCCCFEWyAFRg2zZs1i2LBhFBQUMGLECJ544gkqKysb9djPPvuMWbNmYZomv/71ryO6a6cQQojIJgVGDXFxcWzcuJGFCxfi8/l47LHHuPXWWzl9+vRlH5efn8/kyZMpKSlhzpw5/OIXv2iliIUQQojQIwVGHdxuN7///e9Zs2YNSUlJrF27lgEDBrB58+ZL7uf1etm1axcrVqxg0qRJHD9+nFGjRrF48WLp7yCEECKiycXOLmPKlCls3bqVGTNmsGXLFkaMGMEDDzxAYWEh27dvZ9euXZSVlVXdv3fv3rz++ut4PB4boxZCCCHsJwVGAzIzM/n444/5+c9/zh/+8AeeeeaZS27PysqiX79+XHvttcyfP5/27dvbE6gQQggRQqTAaASPx8NTTz3F2LFjee+998jJyaFfv3706dOHdu3a2R2eEEIIEXKkwGiC8ePHV21jFUIIIUT9ZJGnEEIIIYJOCgwhhBBCBJ0UGEIIIYQIOikwhBBCCBF0UmAIIYQQIuikwBBCCCFE0EmBIYQQQoigkwJDCCGEEEEnBYYQQgghgk4KDCGEEEIEnRQYQgghhAg6KTCEEEIIEXSGaZp2xyCEEEKIMCMjGEIIIYQIOikwhBBCCBF0UmAIIYQQIuikwBBCCCFE0EmBIYQQQoigkwJDCCGEEEEnBYYQQgghgk4KDCGEEEIEnRQYQaSUyvZ/NeyOpa1QSg2VfDWNUmqCUqqz3XG0JUqpbnbH0NYopWL9X+X12UhKqTj/V8kZ0skzKJRSU4B5wEda69/ZHU9boJTqD/wR+B/g37XW5TaHFPKUUhOAhcBw4Gqtdb69EYU+pdR44CGgDPhvYL3W+oK9UYU2pdQtwJ3At8BTWuujNocU8pRSY4G5wBHgd1rrIzaHFBJkBKOZlFKjgF8CT2itf6eUirE7pjbieuC3WuufA93tDiZUBT4JKaWmA/cCPwH+ANxuZ1xtgVLKCfwYWAL8ClBAnHy6rJ9SygPcDSwCvgLmK6WG2RtVm/AD4C/ATuDHSqkbbI4nJEiBcQWUUlFKqST/tyeAFcBDSqn3geVKqRuVUi77Igw9/py1r/ajUmC8UmoD8L+VUg8HppiERSkVBQSeZ59orWdorXcDG4AY/+2imhrPs67ADqxPlYeAa4EYwG1LcCGqRs56AEe11huB/8LK4QSlVAe74gtFSqlYpdRApVSyv5A9CmwBlgH7gUEyjSkFRpMppWYBu4D/ANBa7wT2Al9orW8GXgYmIJ/Kq9TMmV8s0Al4FHgAawh7UutHF5qq5exxAK31sWo3xwHttdZlSil5DftVy9lvAPxTSBuwhvu3AqewhrEftCnEkFNHznYBXZRSM/3TlieBRKzCTABKqdnAJ8CPgFe01j4gHhjmz9lWIBorbxFN/jg1gVIqGuuP+1z/9xP9N+VprX8LoLVeBXQBetoSZIipkTOjWs4+AZxAF611IZAPeP2Piegh7DqeZ+P9XwOjYu8BNymlMrXWlfZEGVrqe21qrT8A3gX+pLWeDawFPEophzzPauXsFv9NTwHZSql1WO8RHYB0W4IMMUqpRKyRsHu01g8ABUqpaVjre36olIr1f+jMBL5nW6AhQobxG6CUygQeA94Gtmut/+L/eQwwSym1XmtdXO3+7bBelKV2xBsKGsjZD5RSuVrrrUqpPGCAf+X1RKw5X7TWEbfyuIGc3amUeldr7VVKubXWF5VSa4C+WEP/EamRr81KoA/gVkoNxRotOxuphVkjcvaO1vrvwN+VUola60Kl1G+As/ZFba9qOVsHfK61frRacboTKNNab/Iv9v+JUmoz1giG056IQ4eMYFyGUsoN/DvWauo04E/Vbt6ANXx4X+C+Sqm7sZ6EW7XW77dyuCGhETk7gf8TE/AK8CowFNiktf5VK4YaMpryPNNaV/jv7wQOtHKoIaORObvf//0rwEHg/wBbtNY/bb1IQ0cjX5s/9N/XAHoopXKBQq31/lYONyTUyFknrMWv1T8EZfHdB/XfYe2Km4dVvK1t3WhDj2xTrYN/yKsD8D7wF631KP/PlwK7tda/978AM7HmLj8F1mA90U75h/wjyhXm7AOt9Q6llNM/jxlRriBn/wN86M+ZW2tdYVfsdrnC59n7WutdSilPJG6HvsLnWS5wGHBG4rbeBnK2S2v9pH+0+t/8/6Zh5XKHUsqltfbaFXsokRGMapRSqUqptcAMoDdwM3BCKXWv/y6/AqYrpVL9FWw74PvAVMCjtT4QacVFM3J2G1AOEGnFRTOfZ4GcRVRx0cznmQ8g0oqLZuRsGoDWuiTSiotG5ux2pVQqUASMxBq1vgv/tLgUF9+RAuNSJrBYa30H1o6H3sDrQB+lVI7W+husyn6cf8HdIGCh1vqmSB1C5MpzNkprvde2qO0lOWs6yVnTNefv2T7borZXY3L2LjAKaxFne6xmZJMj+D2gXrLI81KnsZ48aK1PKaXSsKrU/VgdFH+M1Zdgm79K/S+7Ag0hkrOmk5w1neSs6SRnTdeYnLXHmg45CPSyKc42QdZg1ME/H5kI/LfWeoL/Z4ux9oJ7sBaPFUXibof6SM6aTnLWdJKzppOcNZ3kLDhkBKN+LuATpdQgYDzwArBPax2x27UaQXLWdJKzppOcNZ3krOkkZ80kIxj1UNaFpd4E8oCXtdYv2RxSyJOcNZ3krOkkZ00nOWs6yVnzyQhG/c4A/wr8MdJWnzeD5KzpJGdNJzlrOslZ00nOmkkKjPpt0Vp/ancQbYzkrOkkZ00nOWs6yVnTSc6aSaZIhBBCCBF00gdDCCGEEEEnBYYQQgghgk4KDCGEEEIEnRQYQgghhAg62UUihGiQUioWeAzI11r/VSk1B6u19M+01r+3NTghREiSEQwhRGPEAr8E5vi/3wjMAt6yKyAhRGiTEQwhRGNo/9cblVImcAjIBH4G7FVK5QMdgGXAbOAT4DlgCdbfmXu11uuVUh7gt1jFSRzwHvCA1vpkK/4uQohWICMYQojG+Ff/191YxUFd0yJx/q+bgYnA88ATQEfgP/23/QvWVSnfAp4GJgCLWiRiIYStpMAQQjTGu/6vJ7TWK4ALddynEngEeN3//Uta62eAI0CW/2e3+L/Ow5pyiQPGtEjEQghbyRSJEKIxGtPyt0RrXa6UqvB/X+j/6gOc1e7nxSo0fP7v5YOOEGFIXthCiMY4jzVC0UMpdSfW+osrsRbrg809QDesy2DPC0qEQoiQIgWGEKJBWusKrPUU7YHlfDf60FT/13+cG7AWgU7A2pEihAgzcrEzIYQQQgSdjGAIIYQQIuikwBBCCCFE0EmBIYQQQoigkwJDCCGEEEEnBYYQQgghgk4KDCGEEEIEnRQYQgghhAi6/w9kDlGkMah++gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 24\n",
    "eval_model(\n",
    "    model=my_model_ice,\n",
    "    n=n,\n",
    "    actual_series=series_ice_transformed[\n",
    "        train_ice.end_time() - (2 * n - 1) * train_ice.freq :\n",
    "    ],\n",
    "    val_series=val_ice_transformed,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Backtesting\n",
    "Let's backtest our `TFTModel` model, to see how it performs with a forecast horizon of 12 months over the last 2 years:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b02a42ed4dcc4420809f54452f8b2ed7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the backtest predictions with the two models\n",
    "last_points_only = False\n",
    "backtest_series_ice = my_model_ice.historical_forecasts(\n",
    "    series_ice_transformed,\n",
    "    num_samples=num_samples,\n",
    "    start=training_cutoff_ice,\n",
    "    forecast_horizon=forecast_horizon_ice,\n",
    "    stride=1 if last_points_only else forecast_horizon_ice,\n",
    "    retrain=False,\n",
    "    last_points_only=last_points_only,\n",
    "    overlap_end=True,\n",
    "    verbose=True,\n",
    ")\n",
    "\n",
    "backtest_series_ice = (\n",
    "    concatenate(backtest_series_ice)\n",
    "    if isinstance(backtest_series_ice, list)\n",
    "    else backtest_series_ice\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE: 4.26%\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "eval_backtest(\n",
    "    backtest_series=backtest_series_ice,\n",
    "    actual_series=series_ice_transformed[\n",
    "        train_ice.start_time() - 2 * forecast_horizon_ice * train_ice.freq :\n",
    "    ],\n",
    "    horizon=forecast_horizon_ice,\n",
    "    start=training_cutoff_ice,\n",
    "    transformer=transformer_ice,\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
